Uses of Interface
weka.core.RevisionHandler

Packages that use RevisionHandler

weka.associations
weka.associations.gsp
weka.associations.tertius
weka.attributeSelection
weka.classifiers
weka.classifiers.bayes
weka.classifiers.bayes.blr
weka.classifiers.bayes.net
weka.classifiers.bayes.net.estimate
weka.classifiers.bayes.net.search
weka.classifiers.bayes.net.search.ci
weka.classifiers.bayes.net.search.fixed
weka.classifiers.bayes.net.search.global
weka.classifiers.bayes.net.search.local
weka.classifiers.evaluation
weka.classifiers.functions
weka.classifiers.functions.neural
weka.classifiers.functions.pace
weka.classifiers.functions.supportVector
weka.classifiers.lazy
weka.classifiers.lazy.kstar
weka.classifiers.meta
weka.classifiers.meta.nestedDichotomies
weka.classifiers.mi
weka.classifiers.mi.supportVector
weka.classifiers.misc
weka.classifiers.pmml.consumer
weka.classifiers.rules
weka.classifiers.rules.part
weka.classifiers.trees
weka.classifiers.trees.adtree
weka.classifiers.trees.ft
weka.classifiers.trees.j48
weka.classifiers.trees.lmt
weka.classifiers.trees.m5
weka.classifiers.xml
weka.clusterers
weka.clusterers.forOPTICSAndDBScan.Databases
weka.clusterers.forOPTICSAndDBScan.DataObjects
weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI
weka.clusterers.forOPTICSAndDBScan.Utils
weka.core
weka.core.converters
weka.core.logging
weka.core.matrix
weka.core.neighboursearch
weka.core.neighboursearch.balltrees
weka.core.neighboursearch.covertrees
weka.core.neighboursearch.kdtrees
weka.core.stemmers
weka.core.tokenizers
weka.core.xml
weka.datagenerators
weka.datagenerators.classifiers.classification
weka.datagenerators.classifiers.regression
weka.datagenerators.clusterers
weka.estimators
weka.experiment
weka.experiment.xml
weka.filters
weka.filters.supervised.attribute
weka.filters.supervised.instance
weka.filters.unsupervised.attribute
weka.filters.unsupervised.instance
weka.gui.beans
weka.gui.beans.xml
weka.gui.sql

Uses of RevisionHandler in weka.associations

Classes in weka.associations that implement RevisionHandler

class	AbstractAssociator Abstract scheme for learning associations.
class	Apriori Class implementing an Apriori-type algorithm.
class	AprioriItemSet Class for storing a set of items.
class	AssociatorEvaluation Class for evaluating Associaters.
class	CaRuleGeneration Class implementing the rule generation procedure of the predictive apriori algorithm for class association rules.
class	CheckAssociator Class for examining the capabilities and finding problems with associators.
class	FilteredAssociator Class for running an arbitrary associator on data that has been passed through an arbitrary filter.
class	FPGrowth Class implementing the FP-growth algorithm for finding large item sets without candidate generation.
class	GeneralizedSequentialPatterns Class implementing a GSP algorithm for discovering sequential patterns in a sequential data set. The attribute identifying the distinct data sequences contained in the set can be determined by the respective option.
class	ItemSet Class for storing a set of items.
class	LabeledItemSet Class for storing a set of items together with a class label.
class	PredictiveApriori Class implementing the predictive apriori algorithm to mine association rules. It searches with an increasing support threshold for the best 'n' rules concerning a support-based corrected confidence value. For more information see: Tobias Scheffer: Finding Association Rules That Trade Support Optimally against Confidence.
class	PriorEstimation Class implementing the prior estimattion of the predictive apriori algorithm for mining association rules.
class	RuleGeneration Class implementing the rule generation procedure of the predictive apriori algorithm.
class	RuleItem Class for storing an (class) association rule.
class	SingleAssociatorEnhancer Abstract utility class for handling settings common to meta associators that use a single base associator.
class	Tertius Finds rules according to confirmation measure (Tertius-type algorithm). For more information see: P.

Uses of RevisionHandler in weka.associations.gsp

Classes in weka.associations.gsp that implement RevisionHandler

class	Element Class representing an Element, i.e., a set of events/items.
class	Sequence Class representing a sequence of elements/itemsets.

Uses of RevisionHandler in weka.associations.tertius

Classes in weka.associations.tertius that implement RevisionHandler

class	AttributeValueLiteral
class	Body Class representing the body of a rule.
class	Head Class representing the head of a rule.
class	IndividualInstance
class	IndividualInstances
class	IndividualLiteral
class	Literal
class	LiteralSet Class representing a set of literals, being either the body or the head of a rule.
class	Predicate
class	SimpleLinkedList
class	SimpleLinkedList.LinkedListInverseIterator
class	SimpleLinkedList.LinkedListIterator

Uses of RevisionHandler in weka.attributeSelection

Classes in weka.attributeSelection that implement RevisionHandler

class	ASEvaluation Abstract attribute selection evaluation class
class	ASSearch Abstract attribute selection search class.
class	AttributeSetEvaluator Abstract attribute set evaluator.
class	BestFirst BestFirst: Searches the space of attribute subsets by greedy hillclimbing augmented with a backtracking facility.
class	BestFirst.Link2 Class for a node in a linked list.
class	BestFirst.LinkedList2 Class for handling a linked list.
class	CfsSubsetEval CfsSubsetEval : Evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them. Subsets of features that are highly correlated with the class while having low intercorrelation are preferred. For more information see: M.
class	CheckAttributeSelection Class for examining the capabilities and finding problems with attribute selection schemes.
class	ChiSquaredAttributeEval ChiSquaredAttributeEval : Evaluates the worth of an attribute by computing the value of the chi-squared statistic with respect to the class. Valid options are:
class	ClassifierSubsetEval Classifier subset evaluator: Evaluates attribute subsets on training data or a seperate hold out testing set.
class	ConsistencySubsetEval ConsistencySubsetEval : Evaluates the worth of a subset of attributes by the level of consistency in the class values when the training instances are projected onto the subset of attributes.
class	ConsistencySubsetEval.hashKey Class providing keys to the hash table.
class	CostSensitiveASEvaluation Abstract base class for cost-sensitive subset and attribute evaluators.
class	CostSensitiveAttributeEval A meta subset evaluator that makes its base subset evaluator cost-sensitive.
class	CostSensitiveSubsetEval A meta subset evaluator that makes its base subset evaluator cost-sensitive.
class	ExhaustiveSearch ExhaustiveSearch : Performs an exhaustive search through the space of attribute subsets starting from the empty set of attrubutes.
class	FilteredAttributeEval Class for running an arbitrary attribute evaluator on data that has been passed through an arbitrary filter (note: filters that alter the order or number of attributes are not allowed).
class	FilteredSubsetEval Class for running an arbitrary subset evaluator on data that has been passed through an arbitrary filter (note: filters that alter the order or number of attributes are not allowed).
class	GainRatioAttributeEval GainRatioAttributeEval : Evaluates the worth of an attribute by measuring the gain ratio with respect to the class. GainR(Class, Attribute) = (H(Class) - H(Class | Attribute)) / H(Attribute). Valid options are:
class	GreedyStepwise GreedyStepwise : Performs a greedy forward or backward search through the space of attribute subsets.
class	HoldOutSubsetEvaluator Abstract attribute subset evaluator capable of evaluating subsets with respect to a data set that is distinct from that used to initialize/ train the subset evaluator.
class	InfoGainAttributeEval InfoGainAttributeEval : Evaluates the worth of an attribute by measuring the information gain with respect to the class. InfoGain(Class,Attribute) = H(Class) - H(Class | Attribute). Valid options are:
class	LatentSemanticAnalysis Performs latent semantic analysis and transformation of the data.
class	LFSMethods
class	LFSMethods.Link2 Class for a node in a linked list.
class	LFSMethods.LinkedList2 Class for handling a linked list.
class	LinearForwardSelection LinearForwardSelection: Extension of BestFirst.
class	OneRAttributeEval OneRAttributeEval : Evaluates the worth of an attribute by using the OneR classifier. Valid options are:
class	RaceSearch Races the cross validation error of competing attribute subsets.
class	RandomSearch RandomSearch : Performs a Random search in the space of attribute subsets.
class	Ranker Ranker : Ranks attributes by their individual evaluations.
class	RankSearch RankSearch : Uses an attribute/subset evaluator to rank all attributes.
class	ReliefFAttributeEval ReliefFAttributeEval : Evaluates the worth of an attribute by repeatedly sampling an instance and considering the value of the given attribute for the nearest instance of the same and different class.
class	ScatterSearchV1 Class for performing the Sequential Scatter Search.
class	SubsetSizeForwardSelection SubsetSizeForwardSelection: Extension of LinearForwardSelection.
class	SVMAttributeEval SVMAttributeEval : Evaluates the worth of an attribute by using an SVM classifier.
class	SymmetricalUncertAttributeEval SymmetricalUncertAttributeEval : Evaluates the worth of an attribute by measuring the symmetrical uncertainty with respect to the class.
class	UnsupervisedAttributeEvaluator Abstract unsupervised attribute evaluator.
class	UnsupervisedSubsetEvaluator Abstract unsupervised attribute subset evaluator.
class	WrapperSubsetEval WrapperSubsetEval: Evaluates attribute sets by using a learning scheme.

Uses of RevisionHandler in weka.classifiers

Classes in weka.classifiers that implement RevisionHandler

class	BVDecompose Class for performing a Bias-Variance decomposition on any classifier using the method specified in: Ron Kohavi, David H.
class	BVDecomposeSegCVSub This class performs Bias-Variance decomposion on any classifier using the sub-sampled cross-validation procedure as specified in (1). The Kohavi and Wolpert definition of bias and variance is specified in (2). The Webb definition of bias and variance is specified in (3). Geoffrey I.
class	CheckClassifier Class for examining the capabilities and finding problems with classifiers.
class	CheckSource A simple class for checking the source generated from Classifiers implementing the weka.classifiers.Sourcable interface.
class	Classifier Abstract classifier.
class	CostMatrix Class for storing and manipulating a misclassification cost matrix.
class	Evaluation Class for evaluating machine learning models.
class	IteratedSingleClassifierEnhancer Abstract utility class for handling settings common to meta classifiers that build an ensemble from a single base learner.
class	MultipleClassifiersCombiner Abstract utility class for handling settings common to meta classifiers that build an ensemble from multiple classifiers.
class	RandomizableClassifier Abstract utility class for handling settings common to randomizable classifiers.
class	RandomizableIteratedSingleClassifierEnhancer Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from a single base learner.
class	RandomizableMultipleClassifiersCombiner Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from multiple classifiers based on a given random number seed.
class	RandomizableSingleClassifierEnhancer Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from a single base learner.
class	SingleClassifierEnhancer Abstract utility class for handling settings common to meta classifiers that use a single base learner.

Uses of RevisionHandler in weka.classifiers.bayes

Classes in weka.classifiers.bayes that implement RevisionHandler

class	AODE AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less detrimental) independence assumptions than naive Bayes.
class	AODEsr AODEsr augments AODE with Subsumption Resolution.AODEsr detects specializations between two attribute values at classification time and deletes the generalization attribute value. For more information, see: Fei Zheng, Geoffrey I.
class	BayesianLogisticRegression Implements Bayesian Logistic Regression for both Gaussian and Laplace Priors. For more information, see Alexander Genkin, David D.
class	BayesNet Bayes Network learning using various search algorithms and quality measures. Base class for a Bayes Network classifier.
class	ComplementNaiveBayes Class for building and using a Complement class Naive Bayes classifier. For more information see, Jason D.
class	DMNBtext Class for building and using a Discriminative Multinomial Naive Bayes classifier.
class	HNB Contructs Hidden Naive Bayes classification model with high classification accuracy and AUC. For more information refer to: H.
class	NaiveBayes Class for a Naive Bayes classifier using estimator classes.
class	NaiveBayesMultinomial Class for building and using a multinomial Naive Bayes classifier.
class	NaiveBayesMultinomialUpdateable Class for building and using a multinomial Naive Bayes classifier.
class	NaiveBayesSimple Class for building and using a simple Naive Bayes classifier.Numeric attributes are modelled by a normal distribution. For more information, see Richard Duda, Peter Hart (1973).
class	NaiveBayesUpdateable Class for a Naive Bayes classifier using estimator classes.
class	WAODE WAODE contructs the model called Weightily Averaged One-Dependence Estimators. For more information, see L.

Uses of RevisionHandler in weka.classifiers.bayes.blr

Classes in weka.classifiers.bayes.blr that implement RevisionHandler

class	GaussianPriorImpl Implementation of the Gaussian Prior update function based on CLG Algorithm with a certain Trust Region Update.
class	LaplacePriorImpl Implementation of the Gaussian Prior update function based on modified CLG Algorithm (CLG-Lasso) with a certain Trust Region Update based on Laplace Priors.
class	Prior This is an interface to plug various priors into the Bayesian Logistic Regression Model.

Uses of RevisionHandler in weka.classifiers.bayes.net

Classes in weka.classifiers.bayes.net that implement RevisionHandler

class	ADNode The ADNode class implements the ADTree datastructure which increases the speed with which sub-contingency tables can be constructed from a data set in an Instances object.
class	BayesNetGenerator Bayes Network learning using various search algorithms and quality measures. Base class for a Bayes Network classifier.
class	BIFReader Builds a description of a Bayes Net classifier stored in XML BIF 0.3 format. For more details on XML BIF see: Fabio Cozman, Marek Druzdzel, Daniel Garcia (1998).
class	EditableBayesNet Bayes Network learning using various search algorithms and quality measures. Base class for a Bayes Network classifier.
class	MarginCalculator
class	MarginCalculator.JunctionTreeNode
class	MarginCalculator.JunctionTreeSeparator
class	ParentSet Helper class for Bayes Network classifiers.
class	VaryNode Part of ADTree implementation.

Uses of RevisionHandler in weka.classifiers.bayes.net.estimate

Classes in weka.classifiers.bayes.net.estimate that implement RevisionHandler

class	BayesNetEstimator BayesNetEstimator is the base class for estimating the conditional probability tables of a Bayes network once the structure has been learned.
class	BMAEstimator BMAEstimator estimates conditional probability tables of a Bayes network using Bayes Model Averaging (BMA).
class	DiscreteEstimatorBayes Symbolic probability estimator based on symbol counts and a prior.
class	DiscreteEstimatorFullBayes Symbolic probability estimator based on symbol counts and a prior.
class	MultiNomialBMAEstimator Multinomial BMA Estimator.
class	SimpleEstimator SimpleEstimator is used for estimating the conditional probability tables of a Bayes network once the structure has been learned.

Uses of RevisionHandler in weka.classifiers.bayes.net.search

Classes in weka.classifiers.bayes.net.search that implement RevisionHandler

class

SearchAlgorithm This is the base class for all search algorithms for learning Bayes networks.

Uses of RevisionHandler in weka.classifiers.bayes.net.search.ci

Classes in weka.classifiers.bayes.net.search.ci that implement RevisionHandler

class	CISearchAlgorithm The CISearchAlgorithm class supports Bayes net structure search algorithms that are based on conditional independence test (as opposed to for example score based of cross validation based search algorithms).
class	ICSSearchAlgorithm This Bayes Network learning algorithm uses conditional independence tests to find a skeleton, finds V-nodes and applies a set of rules to find the directions of the remaining arrows.

Uses of RevisionHandler in weka.classifiers.bayes.net.search.fixed

Classes in weka.classifiers.bayes.net.search.fixed that implement RevisionHandler

class

FromFile The FromFile reads the structure of a Bayes net from a file in BIFF format.

Uses of RevisionHandler in weka.classifiers.bayes.net.search.global

Classes in weka.classifiers.bayes.net.search.global that implement RevisionHandler

class

GlobalScoreSearchAlgorithm This Bayes Network learning algorithm uses cross validation to estimate classification accuracy.

Uses of RevisionHandler in weka.classifiers.bayes.net.search.local

Classes in weka.classifiers.bayes.net.search.local that implement RevisionHandler

class	GeneticSearch This Bayes Network learning algorithm uses genetic search for finding a well scoring Bayes network structure.
class	HillClimber This Bayes Network learning algorithm uses a hill climbing algorithm adding, deleting and reversing arcs.
class	K2 This Bayes Network learning algorithm uses a hill climbing algorithm restricted by an order on the variables. For more information see: G.F.
class	LAGDHillClimber This Bayes Network learning algorithm uses a Look Ahead Hill Climbing algorithm called LAGD Hill Climbing.
class	LocalScoreSearchAlgorithm The ScoreBasedSearchAlgorithm class supports Bayes net structure search algorithms that are based on maximizing scores (as opposed to for example conditional independence based search algorithms).
class	RepeatedHillClimber This Bayes Network learning algorithm repeatedly uses hill climbing starting with a randomly generated network structure and return the best structure of the various runs.
class	SimulatedAnnealing This Bayes Network learning algorithm uses the general purpose search method of simulated annealing to find a well scoring network structure. For more information see: R.R.
class	TabuSearch This Bayes Network learning algorithm uses tabu search for finding a well scoring Bayes network structure.
class	TAN This Bayes Network learning algorithm determines the maximum weight spanning tree and returns a Naive Bayes network augmented with a tree. For more information see: N.

Uses of RevisionHandler in weka.classifiers.evaluation

Classes in weka.classifiers.evaluation that implement RevisionHandler

class	ConfusionMatrix Cells of this matrix correspond to counts of the number (or weight) of predictions for each actual value / predicted value combination.
class	CostCurve Generates points illustrating probablity cost tradeoffs that can be obtained by varying the threshold value between classes.
class	EvaluationUtils Contains utility functions for generating lists of predictions in various manners.
class	MarginCurve Generates points illustrating the prediction margin.
class	NominalPrediction Encapsulates an evaluatable nominal prediction: the predicted probability distribution plus the actual class value.
class	NumericPrediction Encapsulates an evaluatable numeric prediction: the predicted class value plus the actual class value.
class	ThresholdCurve Generates points illustrating prediction tradeoffs that can be obtained by varying the threshold value between classes.
class	TwoClassStats Encapsulates performance functions for two-class problems.

Uses of RevisionHandler in weka.classifiers.functions

Classes in weka.classifiers.functions that implement RevisionHandler

class	GaussianProcesses Implements Gaussian Processes for regression without hyperparameter-tuning.
class	IsotonicRegression Learns an isotonic regression model.
class	LeastMedSq Implements a least median sqaured linear regression utilising the existing weka LinearRegression class to form predictions.
class	LibLINEAR A wrapper class for the liblinear tools (the liblinear classes, typically the jar file, need to be in the classpath to use this classifier). Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin (2008).
class	LibSVM A wrapper class for the libsvm tools (the libsvm classes, typically the jar file, need to be in the classpath to use this classifier). LibSVM runs faster than SMO since it uses LibSVM to build the SVM classifier. LibSVM allows users to experiment with One-class SVM, Regressing SVM, and nu-SVM supported by LibSVM tool.
class	LinearRegression Class for using linear regression for prediction.
class	Logistic Class for building and using a multinomial logistic regression model with a ridge estimator. There are some modifications, however, compared to the paper of leCessie and van Houwelingen(1992): If there are k classes for n instances with m attributes, the parameter matrix B to be calculated will be an m*(k-1) matrix. The probability for class j with the exception of the last class is Pj(Xi) = exp(XiBj)/((sum[j=1..(k-1)]exp(Xi*Bj))+1) The last class has probability 1-(sum[j=1..(k-1)]Pj(Xi)) = 1/((sum[j=1..(k-1)]exp(Xi*Bj))+1) The (negative) multinomial log-likelihood is thus: L = -sum[i=1..n]{ sum[j=1..(k-1)](Yij * ln(Pj(Xi))) +(1 - (sum[j=1..(k-1)]Yij)) * ln(1 - sum[j=1..(k-1)]Pj(Xi)) } + ridge * (B^2) In order to find the matrix B for which L is minimised, a Quasi-Newton Method is used to search for the optimized values of the m*(k-1) variables.
class	MultilayerPerceptron A Classifier that uses backpropagation to classify instances. This network can be built by hand, created by an algorithm or both.
class	PaceRegression Class for building pace regression linear models and using them for prediction.
class	PLSClassifier A wrapper classifier for the PLSFilter, utilizing the PLSFilter's ability to perform predictions.
class	RBFNetwork Class that implements a normalized Gaussian radial basisbasis function network. It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that.
class	SimpleLinearRegression Learns a simple linear regression model.
class	SimpleLogistic Classifier for building linear logistic regression models.
class	SMO Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier. This implementation globally replaces all missing values and transforms nominal attributes into binary ones.
class	SMOreg SMOreg implements the support vector machine for regression.
class	SPegasos Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al.
class	VotedPerceptron Implementation of the voted perceptron algorithm by Freund and Schapire.
class	Winnow Implements Winnow and Balanced Winnow algorithms by Littlestone. For more information, see N.

Uses of RevisionHandler in weka.classifiers.functions.neural

Classes in weka.classifiers.functions.neural that implement RevisionHandler

class	LinearUnit This can be used by the neuralnode to perform all it's computations (as a Linear unit).
class	NeuralConnection Abstract unit in a NeuralNetwork.
class	NeuralNode This class is used to represent a node in the neuralnet.
class	SigmoidUnit This can be used by the neuralnode to perform all it's computations (as a sigmoid unit).

Uses of RevisionHandler in weka.classifiers.functions.pace

Classes in weka.classifiers.functions.pace that implement RevisionHandler

class	ChisqMixture Class for manipulating chi-square mixture distributions.
class	DiscreteFunction Class for handling discrete functions.
class	MixtureDistribution Abtract class for manipulating mixture distributions.
class	NormalMixture Class for manipulating normal mixture distributions.
class	PaceMatrix Class for matrix manipulation used for pace regression.

Uses of RevisionHandler in weka.classifiers.functions.supportVector

Classes in weka.classifiers.functions.supportVector that implement RevisionHandler

class	CachedKernel Base class for RBFKernel and PolyKernel that implements a simple LRU.
class	CheckKernel Class for examining the capabilities and finding problems with kernels.
class	Kernel Abstract kernel.
class	KernelEvaluation Class for evaluating Kernels.
class	NormalizedPolyKernel The normalized polynomial kernel. K(x,y) = <x,y>/sqrt(<x,x><y,y>) where <x,y> = PolyKernel(x,y) Valid options are:
class	PolyKernel The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1)^p Valid options are:
class	PrecomputedKernelMatrixKernel This kernel is based on a static kernel matrix that is read from a file.
class	Puk The Pearson VII function-based universal kernel. For more information see: B.
class	RBFKernel The RBF kernel.
class	RegOptimizer Base class implementation for learning algorithm of SMOreg Valid options are:
class	RegSMO Implementation of SMO for support vector regression as described in : A.J.
class	RegSMOImproved Learn SVM for regression using SMO with Shevade, Keerthi, et al.
class	SMOset Stores a set of integer of a given size.
class	StringKernel Implementation of the subsequence kernel (SSK) as described in [1] and of the subsequence kernel with lambda pruning (SSK-LP) as described in [2]. For more information, see Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Christopher J.

Uses of RevisionHandler in weka.classifiers.lazy

Classes in weka.classifiers.lazy that implement RevisionHandler

class	IB1 Nearest-neighbour classifier.
class	IBk K-nearest neighbours classifier.
class	KStar K* is an instance-based classifier, that is the class of a test instance is based upon the class of those training instances similar to it, as determined by some similarity function.
class	LBR Lazy Bayesian Rules Classifier.
class	LBR.Indexes Class for handling instances and the associated attributes.
class	LWL Locally weighted learning.

Uses of RevisionHandler in weka.classifiers.lazy.kstar

Classes in weka.classifiers.lazy.kstar that implement RevisionHandler

class	KStarCache A class representing the caching system used to keep track of each attribute value and its corresponding scale factor or stop parameter.
class	KStarCache.CacheTable A custom hashtable class to support the caching system.
class	KStarCache.TableEntry Hashtable collision list.
class	KStarNominalAttribute A custom class which provides the environment for computing the transformation probability of a specified test instance nominal attribute to a specified train instance nominal attribute.
class	KStarNumericAttribute A custom class which provides the environment for computing the transformation probability of a specified test instance numeric attribute to a specified train instance numeric attribute.
class	KStarWrapper

Uses of RevisionHandler in weka.classifiers.meta

Classes in weka.classifiers.meta that implement RevisionHandler

class	AdaBoostM1 Class for boosting a nominal class classifier using the Adaboost M1 method.
class	AdditiveRegression Meta classifier that enhances the performance of a regression base classifier.
class	AttributeSelectedClassifier Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
class	Bagging Class for bagging a classifier to reduce variance.
class	ClassificationViaClustering A simple meta-classifier that uses a clusterer for classification.
class	ClassificationViaRegression Class for doing classification using regression methods.
class	CostSensitiveClassifier A metaclassifier that makes its base classifier cost-sensitive.
class	CVParameterSelection Class for performing parameter selection by cross-validation for any classifier. For more information, see: R.
class	Dagging This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier.
class	Decorate DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples.
class	END A meta classifier for handling multi-class datasets with 2-class classifiers by building an ensemble of nested dichotomies. For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.
class	FilteredClassifier Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
class	Grading Implements Grading.
class	GridSearch Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting. The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy).
class	LogitBoost Class for performing additive logistic regression.
class	MetaCost This metaclassifier makes its base classifier cost-sensitive using the method specified in Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive.
class	MultiBoostAB Class for boosting a classifier using the MultiBoosting method. MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees.
class	MultiClassClassifier A metaclassifier for handling multi-class datasets with 2-class classifiers.
class	MultiScheme Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
class	OrdinalClassClassifier Meta classifier that allows standard classification algorithms to be applied to ordinal class problems. For more information see: Eibe Frank, Mark Hall: A Simple Approach to Ordinal Classification.
class	RacedIncrementalLogitBoost Classifier for incremental learning of large datasets by way of racing logit-boosted committees. For more information see: Eibe Frank, Geoffrey Holmes, Richard Kirkby, Mark Hall: Racing committees for large datasets.
class	RandomCommittee Class for building an ensemble of randomizable base classifiers.
class	RandomSubSpace This method constructs a decision tree based classifier that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.
class	RegressionByDiscretization A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized.
class	RotationForest Class for construction a Rotation Forest.
class	Stacking Combines several classifiers using the stacking method.
class	StackingC Implements StackingC (more efficient version of stacking). For more information, see A.K.
class	ThresholdSelector A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier.
class	Vote Class for combining classifiers.

Uses of RevisionHandler in weka.classifiers.meta.nestedDichotomies

Classes in weka.classifiers.meta.nestedDichotomies that implement RevisionHandler

class	ClassBalancedND A meta classifier for handling multi-class datasets with 2-class classifiers by building a random class-balanced tree structure. For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.
class	DataNearBalancedND A meta classifier for handling multi-class datasets with 2-class classifiers by building a random data-balanced tree structure. For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.
class	ND A meta classifier for handling multi-class datasets with 2-class classifiers by building a random tree structure. For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.

Uses of RevisionHandler in weka.classifiers.mi

Classes in weka.classifiers.mi that implement RevisionHandler

class	CitationKNN Modified version of the Citation kNN multi instance classifier. For more information see: Jun Wang, Zucker, Jean-Daniel: Solving Multiple-Instance Problem: A Lazy Learning Approach.
class	MDD Modified Diverse Density algorithm, with collective assumption. More information about DD: Oded Maron (1998).
class	MIBoost MI AdaBoost method, considers the geometric mean of posterior of instances inside a bag (arithmatic mean of log-posterior) and the expectation for a bag is taken inside the loss function. For more information about Adaboost, see: Yoav Freund, Robert E.
class	MIDD Re-implement the Diverse Density algorithm, changes the testing procedure. Oded Maron (1998).
class	MIEMDD EMDD model builds heavily upon Dietterich's Diverse Density (DD) algorithm. It is a general framework for MI learning of converting the MI problem to a single-instance setting using EM.
class	MILR Uses either standard or collective multi-instance assumption, but within linear regression.
class	MINND Multiple-Instance Nearest Neighbour with Distribution learner. It uses gradient descent to find the weight for each dimension of each exeamplar from the starting point of 1.0.
class	MIOptimalBall This classifier tries to find a suitable ball in the multiple-instance space, with a certain data point in the instance space as a ball center.
class	MISMO Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier. This implementation globally replaces all missing values and transforms nominal attributes into binary ones.
class	MISVM Implements Stuart Andrews' mi_SVM (Maximum pattern Margin Formulation of MIL).
class	MIWrapper A simple Wrapper method for applying standard propositional learners to multi-instance data. For more information see: E.
class	SimpleMI Reduces MI data into mono-instance data.

Uses of RevisionHandler in weka.classifiers.mi.supportVector

Classes in weka.classifiers.mi.supportVector that implement RevisionHandler

class	MIPolyKernel The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1)^p Valid options are:
class	MIRBFKernel The RBF kernel.

Uses of RevisionHandler in weka.classifiers.misc

Classes in weka.classifiers.misc that implement RevisionHandler

class	HyperPipes Class implementing a HyperPipe classifier.
class	SerializedClassifier A wrapper around a serialized classifier model.
class	VFI Classification by voting feature intervals.

Uses of RevisionHandler in weka.classifiers.pmml.consumer

Classes in weka.classifiers.pmml.consumer that implement RevisionHandler

class	GeneralRegression Class implementing import of PMML General Regression model.
class	NeuralNetwork Class implementing import of PMML Neural Network model.
class	PMMLClassifier Abstract base class for all PMML classifiers.
class	Regression Class implementing import of PMML Regression model.

Uses of RevisionHandler in weka.classifiers.rules

Classes in weka.classifiers.rules that implement RevisionHandler

class	ConjunctiveRule This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels. A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression.
class	DecisionTable Class for building and using a simple decision table majority classifier. For more information see: Ron Kohavi: The Power of Decision Tables.
class	DecisionTableHashKey Class providing hash table keys for DecisionTable
class	DTNB Class for building and using a decision table/naive bayes hybrid classifier.
class	JRip This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was proposed by William W.
class	M5Rules Generates a decision list for regression problems using separate-and-conquer.
class	NNge Nearest-neighbor-like algorithm using non-nested generalized exemplars (which are hyperrectangles that can be viewed as if-then rules).
class	OneR Class for building and using a 1R classifier; in other words, uses the minimum-error attribute for prediction, discretizing numeric attributes.
class	PART Class for generating a PART decision list.
class	Prism Class for building and using a PRISM rule set for classification.
class	Ridor An implementation of a RIpple-DOwn Rule learner. It generates a default rule first and then the exceptions for the default rule with the least (weighted) error rate.
class	Rule Abstract class of generic rule
class	RuleStats This class implements the statistics functions used in the propositional rule learner, from the simpler ones like count of true/false positive/negatives, filter data based on the ruleset, etc.
class	ZeroR Class for building and using a 0-R classifier.

Uses of RevisionHandler in weka.classifiers.rules.part

Classes in weka.classifiers.rules.part that implement RevisionHandler

class	C45PruneableDecList Class for handling a partial tree structure pruned using C4.5's pruning heuristic.
class	ClassifierDecList Class for handling a rule (partial tree) for a decision list.
class	MakeDecList Class for handling a decision list.
class	PruneableDecList Class for handling a partial tree structure that can be pruned using a pruning set.

Uses of RevisionHandler in weka.classifiers.trees

Classes in weka.classifiers.trees that implement RevisionHandler

class	ADTree Class for generating an alternating decision tree.
class	BFTree Class for building a best-first decision tree classifier.
class	DecisionStump Class for building and using a decision stump.
class	FT Classifier for building 'Functional trees', which are classification trees that could have logistic regression functions at the inner nodes and/or leaves.
class	Id3 Class for constructing an unpruned decision tree based on the ID3 algorithm.
class	J48 Class for generating a pruned or unpruned C4.5 decision tree.
class	J48graft Class for generating a grafted (pruned or unpruned) C4.5 decision tree.
class	LADTree Class for generating a multi-class alternating decision tree using the LogitBoost strategy.
class	LMT Classifier for building 'logistic model trees', which are classification trees with logistic regression functions at the leaves.
class	M5P M5Base.
class	NBTree Class for generating a decision tree with naive Bayes classifiers at the leaves. For more information, see Ron Kohavi: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid.
class	RandomForest Class for constructing a forest of random trees. For more information see: Leo Breiman (2001).
class	RandomTree Class for constructing a tree that considers K randomly chosen attributes at each node.
class	REPTree Fast decision tree learner.
class	SimpleCart Class implementing minimal cost-complexity pruning. Note when dealing with missing values, use "fractional instances" method instead of surrogate split method. For more information, see: Leo Breiman, Jerome H.
class	UserClassifier Interactively classify through visual means.

Uses of RevisionHandler in weka.classifiers.trees.adtree

Classes in weka.classifiers.trees.adtree that implement RevisionHandler

class	PredictionNode Class representing a prediction node in an alternating tree.
class	ReferenceInstances Simple class that extends the Instances class making it possible to create subsets of instances that reference their source set.
class	Splitter Abstract class representing a splitter node in an alternating tree.
class	TwoWayNominalSplit Class representing a two-way split on a nominal attribute, of the form: either 'is some_value' or 'is not some_value'.
class	TwoWayNumericSplit Class representing a two-way split on a numeric attribute, of the form: either 'is < some_value' or 'is >= some_value'.

Uses of RevisionHandler in weka.classifiers.trees.ft

Classes in weka.classifiers.trees.ft that implement RevisionHandler

class	FTInnerNode Class for Functional Inner tree structure.
class	FTLeavesNode Class for Functional Leaves tree version.
class	FTNode Class for Functional tree structure.
class	FTtree Abstract class for Functional tree structure.

Uses of RevisionHandler in weka.classifiers.trees.j48

Classes in weka.classifiers.trees.j48 that implement RevisionHandler

class	BinC45ModelSelection Class for selecting a C4.5-like binary (!) split for a given dataset.
class	BinC45Split Class implementing a binary C4.5-like split on an attribute.
class	C45ModelSelection Class for selecting a C4.5-type split for a given dataset.
class	C45PruneableClassifierTree Class for handling a tree structure that can be pruned using C4.5 procedures.
class	C45PruneableClassifierTreeG Class for handling a tree structure that can be pruned using C4.5 procedures and have nodes grafted on.
class	C45Split Class implementing a C4.5-type split on an attribute.
class	ClassifierSplitModel Abstract class for classification models that can be used recursively to split the data.
class	ClassifierTree Class for handling a tree structure used for classification.
class	Distribution Class for handling a distribution of class values.
class	EntropyBasedSplitCrit "Abstract" class for computing splitting criteria based on the entropy of a class distribution.
class	EntropySplitCrit Class for computing the entropy for a given distribution.
class	GainRatioSplitCrit Class for computing the gain ratio for a given distribution.
class	GraftSplit Class implementing a split for nodes added to a tree during grafting.
class	InfoGainSplitCrit Class for computing the information gain for a given distribution.
class	ModelSelection Abstract class for model selection criteria.
class	NBTreeClassifierTree Class for handling a naive bayes tree structure used for classification.
class	NBTreeModelSelection Class for selecting a NB tree split.
class	NBTreeNoSplit Class implementing a "no-split"-split (leaf node) for naive bayes trees.
class	NBTreeSplit Class implementing a NBTree split on an attribute.
class	NoSplit Class implementing a "no-split"-split.
class	PruneableClassifierTree Class for handling a tree structure that can be pruned using a pruning set.
class	SplitCriterion Abstract class for computing splitting criteria with respect to distributions of class values.
class	Stats Class implementing a statistical routine needed by J48 to compute its error estimate.

Uses of RevisionHandler in weka.classifiers.trees.lmt

Classes in weka.classifiers.trees.lmt that implement RevisionHandler

class	LMTNode Class for logistic model tree structure.
class	LogisticBase Base/helper class for building logistic regression models with the LogitBoost algorithm.
class	ResidualModelSelection Helper class for logistic model trees (weka.classifiers.trees.lmt.LMT) to implement the splitting criterion based on residuals.
class	ResidualSplit Helper class for logistic model trees (weka.classifiers.trees.lmt.LMT) to implement the splitting criterion based on residuals of the LogitBoost algorithm.

Uses of RevisionHandler in weka.classifiers.trees.m5

Classes in weka.classifiers.trees.m5 that implement RevisionHandler

class	CorrelationSplitInfo Finds split points using correlation.
class	Impurity Class for handling the impurity values when spliting the instances
class	M5Base M5Base.
class	PreConstructedLinearModel This class encapsulates a linear regression function.
class	RuleNode Constructs a node for use in an m5 tree or rule
class	Values Stores some statistics.
class	YongSplitInfo Stores split information.

Uses of RevisionHandler in weka.classifiers.xml

Classes in weka.classifiers.xml that implement RevisionHandler

class

XMLClassifier This class serializes and deserializes a Classifier instance to and fro XML.

Uses of RevisionHandler in weka.clusterers

Classes in weka.clusterers that implement RevisionHandler

class	AbstractClusterer Abstract clusterer.
class	AbstractDensityBasedClusterer Abstract clustering model that produces (for each test instance) an estimate of the membership in each cluster (ie.
class	CheckClusterer Class for examining the capabilities and finding problems with clusterers.
class	CLOPE Yiling Yang, Xudong Guan, Jinyuan You: CLOPE: a fast and effective clustering algorithm for transactional data.
class	ClusterEvaluation Class for evaluating clustering models. Valid options are: -t name of the training file Specify the training file.
class	Cobweb Class implementing the Cobweb and Classit clustering algorithms. Note: the application of node operators (merging, splitting etc.) in terms of ordering and priority differs (and is somewhat ambiguous) between the original Cobweb and Classit papers.
class	DBScan Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise.
class	EM Simple EM (expectation maximisation) class. EM assigns a probability distribution to each instance which indicates the probability of it belonging to each of the clusters.
class	FarthestFirst Cluster data using the FarthestFirst algorithm. For more information see: Hochbaum, Shmoys (1985).
class	FilteredClusterer Class for running an arbitrary clusterer on data that has been passed through an arbitrary filter.
class	HierarchicalClusterer Hierarchical clustering class.
class	MakeDensityBasedClusterer Class for wrapping a Clusterer to make it return a distribution and density.
class	OPTICS Mihael Ankerst, Markus M.
class	RandomizableClusterer Abstract utility class for handling settings common to randomizable clusterers.
class	RandomizableDensityBasedClusterer Abstract utility class for handling settings common to randomizable clusterers.
class	RandomizableSingleClustererEnhancer Abstract utility class for handling settings common to randomizable clusterers.
class	sIB Cluster data using the sequential information bottleneck algorithm. Note: only hard clustering scheme is supported.
class	SimpleKMeans Cluster data using the k means algorithm Valid options are:
class	SingleClustererEnhancer Meta-clusterer for enhancing a base clusterer.
class	XMeans Cluster data using the X-means algorithm. X-Means is K-Means extended by an Improve-Structure part In this part of the algorithm the centers are attempted to be split in its region.

Uses of RevisionHandler in weka.clusterers.forOPTICSAndDBScan.Databases

Classes in weka.clusterers.forOPTICSAndDBScan.Databases that implement RevisionHandler

class

SequentialDatabase SequentialDatabase.java Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert Date: Aug 20, 2004 Time: 1:23:38 PM $ Revision 1.4 $

Uses of RevisionHandler in weka.clusterers.forOPTICSAndDBScan.DataObjects

Classes in weka.clusterers.forOPTICSAndDBScan.DataObjects that implement RevisionHandler

class	EuclidianDataObject EuclidianDataObject.java Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert Date: Aug 19, 2004 Time: 5:50:22 PM $ Revision 1.4 $
class	ManhattanDataObject ManhattanDataObject.java Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert Date: Aug 19, 2004 Time: 5:50:22 PM $ Revision 1.4 $

Uses of RevisionHandler in weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI

Classes in weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI that implement RevisionHandler

class	GraphPanel GraphPanel.java Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht Date: Sep 16, 2004 Time: 10:28:19 AM $ Revision 1.4 $
class	OPTICS_Visualizer Start the OPTICS Visualizer from command-line: java weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.OPTICS_Visualizer [file.ser]
class	ResultVectorTableModel ResultVectorTableModel.java Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht Date: Sep 12, 2004 Time: 9:23:31 PM $ Revision 1.4 $
class	SERFileFilter SERFileFilter.java Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht Date: Sep 15, 2004 Time: 6:54:56 PM $ Revision 1.4 $
class	SERObject SERObject.java Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht Date: Sep 15, 2004 Time: 9:43:00 PM $ Revision 1.4 $

Uses of RevisionHandler in weka.clusterers.forOPTICSAndDBScan.Utils

Classes in weka.clusterers.forOPTICSAndDBScan.Utils that implement RevisionHandler

class	EpsilonRange_ListElement EpsilonRange_ListElement.java Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert Date: Sep 7, 2004 Time: 2:12:34 PM $ Revision 1.4 $
class	PriorityQueue PriorityQueue.java Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert Date: Aug 27, 2004 Time: 5:36:35 PM $ Revision 1.4 $
class	PriorityQueueElement PriorityQueueElement.java Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert Date: Aug 31, 2004 Time: 6:43:18 PM $ Revision 1.4 $
class	UpdateQueue UpdateQueue.java Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert Date: Aug 27, 2004 Time: 5:36:35 PM $ Revision 1.4 $
class	UpdateQueueElement UpdateQueueElement.java Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert Date: Aug 31, 2004 Time: 6:43:18 PM $ Revision 1.4 $

Uses of RevisionHandler in weka.core

Classes in weka.core that implement RevisionHandler

class	AbstractStringDistanceFunction Represents the abstract ancestor for string-based distance functions, like EditDistance.
class	AlgVector Class for performing operations on an algebraic vector of floating-point values.
class	AllJavadoc Applies all known Javadoc-derived classes to a source file.
class	Attribute Class for handling an attribute.
class	AttributeExpression A general purpose class for parsing mathematical expressions involving attribute values.
class	AttributeLocator This class locates and records the indices of a certain type of attributes, recursively in case of Relational attributes.
class	AttributeStats A Utility class that contains summary information on an the values that appear in a dataset for a particular attribute.
class	BinarySparseInstance Class for storing a binary-data-only instance as a sparse vector.
class	Capabilities A class that describes the capabilites (e.g., handling certain types of attributes, missing values, types of classes, etc.) of a specific classifier.
class	ChebyshevDistance Implements the Chebyshev distance.
class	Check Abstract general class for testing in Weka.
class	CheckGOE Simple command line checking of classes that are editable in the GOE. Usage: CheckGOE -W classname -- test options Valid options are:
class	CheckOptionHandler Simple command line checking of classes that implement OptionHandler. Usage: CheckOptionHandler -W optionHandlerClassName -- test options Valid options are:
class	CheckScheme Abstract general class for testing schemes in Weka.
static class	CheckScheme.PostProcessor a class for postprocessing the test-data
class	ClassDiscovery This class is used for discovering classes that implement a certain interface or a derived from a certain class.
static class	ClassDiscovery.StringCompare compares two strings.
class	ClassloaderUtil Utility class that can add jar files to the classpath dynamically.
class	ContingencyTables Class implementing some statistical routines for contingency tables.
class	Debug A helper class for debug output, logging, clocking, etc.
static class	Debug.Clock A little helper class for clocking and outputting times.
static class	Debug.DBO contains debug methods
static class	Debug.Log A helper class for logging stuff.
static class	Debug.Random This extended Random class enables one to print the generated random numbers etc., before they are returned.
static class	Debug.SimpleLog A little, simple helper class for logging stuff.
static class	Debug.Timestamp A class that can be used for timestamps in files, The toString() method simply returns the associated Date object in a timestamp format.
class	EditDistance Computes the Levenshtein edit distance between two strings.
class	Environment This class encapsulates a map of all environment and java system properties.
class	EuclideanDistance Implementing Euclidean distance (or similarity) function. One object defines not one distance but the data model in which the distances between objects of that data model can be computed. Attention: For efficiency reasons the use of consistency checks (like are the data models of the two instances exactly the same), is low. For more information, see: Wikipedia.
class	FastVector Implements a fast vector class without synchronized methods.
class	FastVector.FastVectorEnumeration Class for enumerating the vector's elements.
class	FindWithCapabilities Locates all classes with certain capabilities.
class	GlobalInfoJavadoc Generates Javadoc comments from the class's globalInfo method.
class	Instance Class for handling an instance.
class	InstanceComparator A comparator for the Instance class.
class	Instances Class for handling an ordered set of weighted instances.
class	Javadoc Abstract superclass for classes that generate Javadoc comments and replace the content between certain comment tags.
class	Jython A helper class for Jython.
class	ListOptions Lists the options of an OptionHandler
class	ManhattanDistance Implements the Manhattan distance (or Taxicab geometry).
class	MathematicalExpression Class for evaluating a string adhering the following grammar:
class	Memory A little helper class for Memory management.
class	NormalizableDistance Represents the abstract ancestor for normalizable distance functions, like Euclidean or Manhattan distance.
class	Optimization Implementation of Active-sets method with BFGS update to solve optimization problem with only bounds constraints in multi-dimensions.
class	Option Class to store information about an option.
class	OptionHandlerJavadoc Generates Javadoc comments from the OptionHandler's options.
class	PropertyPath A helper class for accessing properties in nested objects, e.g., accessing the "getRidge" method of a LinearRegression classifier part of MultipleClassifierCombiner, e.g., Vote.
static class	PropertyPath.Path Contains a (property) path structure
static class	PropertyPath.PathElement Represents a single element of a property path
class	ProtectedProperties Simple class that extends the Properties class so that the properties are unable to be modified.
class	Queue Class representing a FIFO queue.
class	RandomVariates Class implementing some simple random variates generator.
class	Range Class representing a range of cardinal numbers.
class	RelationalLocator This class locates and records the indices of relational attributes,
class	SelectedTag Represents a selected value from a finite set of values, where each value is a Tag (i.e.
class	SerializationHelper A helper class for determining serialVersionUIDs and checking whether classes contain one and/or need one.
class	SerializedObject Class for storing an object in serialized form in memory.
class	SingleIndex Class representing a single cardinal number.
class	SparseInstance Class for storing an instance as a sparse vector.
class	SpecialFunctions Class implementing some mathematical functions.
class	Statistics Class implementing some distributions, tests, etc.
class	Stopwords Class that can test whether a given string is a stop word.
class	StringLocator This class locates and records the indices of String attributes, recursively in case of Relational attributes.
class	SystemInfo This class prints some information about the system setup, like Java version, JVM settings etc.
class	Tag A Tag simply associates a numeric ID with a String description.
class	TechnicalInformation Used for paper references in the Javadoc and for BibTex generation.
class	TechnicalInformationHandlerJavadoc Generates Javadoc comments from the TechnicalInformationHandler's data.
class	Tee This class pipelines print/println's to several PrintStreams.
class	TestInstances Generates artificial datasets for testing.
class	Trie A class representing a Trie data structure for strings.
static class	Trie.TrieIterator Represents an iterator over a trie
static class	Trie.TrieNode Represents a node in the trie.
class	Utils Class implementing some simple utility methods.
class	Version This class contains the version number of the current WEKA release and some methods for comparing another version string.

Methods in weka.core with parameters of type RevisionHandler

static String	RevisionUtils.extract(RevisionHandler handler) Extracts the revision string returned by the RevisionHandler.
static RevisionUtils.Type	RevisionUtils.getType(RevisionHandler handler) Determines the type of a (sanitized) revision string returned by the RevisionHandler.

Uses of RevisionHandler in weka.core.converters

Subinterfaces of RevisionHandler in weka.core.converters

interface	Loader Interface to something that can load Instances from an input source in some format.
interface	Saver Interface to something that can save Instances to an output destination in some format.

Classes in weka.core.converters that implement RevisionHandler

class	AbstractFileLoader Abstract superclass for all file loaders.
class	AbstractFileSaver Abstract class for Savers that save to a file Valid options are: -i input arff file The input filw in arff format.
class	AbstractLoader Abstract class gives default implementation of setSource methods.
class	AbstractSaver Abstract class for Saver
class	ArffLoader Reads a source that is in arff (attribute relation file format) format.
static class	ArffLoader.ArffReader Reads data from an ARFF file, either in incremental or batch mode.
class	ArffSaver Writes to a destination in arff text format.
class	C45Loader Reads a file that is C45 format.
class	C45Saver Writes to a destination that is in the format used by the C4.5 algorithm. Therefore it outputs a names and a data file.
class	ConverterUtils Utility routines for the converter package.
static class	ConverterUtils.DataSink Helper class for saving data to files.
static class	ConverterUtils.DataSource Helper class for loading data from files and URLs.
class	CSVLoader Reads a source that is in comma separated or tab separated format.
class	CSVSaver Writes to a destination that is in csv format Valid options are:
class	DatabaseConnection Connects to a database.
class	DatabaseLoader Reads Instances from a Database.
class	DatabaseSaver Writes to a database (tested with MySQL, InstantDB, HSQLDB).
class	LibSVMLoader Reads a source that is in libsvm format. For more information about libsvm see: http://www.csie.ntu.edu.tw/~cjlin/libsvm/
class	LibSVMSaver Writes to a destination that is in libsvm format. For more information about libsvm see: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Valid options are:
class	SerializedInstancesLoader Reads a source that contains serialized Instances.
class	SerializedInstancesSaver Serializes the instances to a file with extension bsi.
class	SVMLightLoader Reads a source that is in svm light format. For more information about svm light see: http://svmlight.joachims.org/
class	SVMLightSaver Writes to a destination that is in svm light format. For more information about svm light see: http://svmlight.joachims.org/ Valid options are:
class	TextDirectoryLoader Loads all text files in a directory and uses the subdirectory names as class labels.
class	XRFFLoader Reads a source that is in the XML version of the ARFF format.
class	XRFFSaver Writes to a destination that is in the XML version of the ARFF format.

Uses of RevisionHandler in weka.core.logging

Classes in weka.core.logging that implement RevisionHandler

class	ConsoleLogger A simple logger that outputs the logging information in the console.
class	FileLogger A simple file logger, that just logs to a single file.
class	Logger Abstract superclass for all loggers.
class	OutputLogger A logger that logs all output on stdout and stderr to a file.

Uses of RevisionHandler in weka.core.matrix

Classes in weka.core.matrix that implement RevisionHandler

class	CholeskyDecomposition Cholesky Decomposition.
class	DoubleVector A vector specialized on doubles.
class	EigenvalueDecomposition Eigenvalues and eigenvectors of a real matrix.
class	ExponentialFormat
class	FlexibleDecimalFormat
class	FloatingPointFormat Class for the format of floating point numbers
class	IntVector A vector specialized on integers.
class	LUDecomposition LU Decomposition.
class	Maths Utility class.
class	Matrix Jama = Java Matrix class.
class	QRDecomposition QR Decomposition.
class	SingularValueDecomposition Singular Value Decomposition.

Uses of RevisionHandler in weka.core.neighboursearch

Classes in weka.core.neighboursearch that implement RevisionHandler

class	BallTree Class implementing the BallTree/Metric Tree algorithm for nearest neighbour search. The connection to dataset is only a reference.
class	CoverTree Class implementing the CoverTree datastructure. The class is very much a translation of the c source code made available by the authors. For more information and original source code see: Alina Beygelzimer, Sham Kakade, John Langford: Cover trees for nearest neighbor.
class	CoverTree.CoverTreeNode class representing a node of the cover tree.
class	KDTree Class implementing the KDTree search algorithm for nearest neighbour search. The connection to dataset is only a reference.
class	LinearNNSearch Class implementing the brute force search algorithm for nearest neighbour search.
class	NearestNeighbourSearch Abstract class for nearest neighbour search.
class	PerformanceStats The class that measures the performance of a nearest neighbour search (NNS) algorithm.
class	TreePerformanceStats The class that measures the performance of a tree based nearest neighbour search algorithm.

Uses of RevisionHandler in weka.core.neighboursearch.balltrees

Classes in weka.core.neighboursearch.balltrees that implement RevisionHandler

class	BallNode Class representing a node of a BallTree.
class	BallSplitter Abstract class for splitting a ball tree's BallNode.
class	BallTreeConstructor Abstract class for constructing a BallTree .
class	BottomUpConstructor The class that constructs a ball tree bottom up.
class	MedianDistanceFromArbitraryPoint Class that splits a BallNode of a ball tree using Uhlmann's described method. For information see: Jeffrey K.
class	MiddleOutConstructor The class that builds a BallTree middle out. For more information see also: Andrew W.
class	PointsClosestToFurthestChildren Implements the Moore's method to split a node of a ball tree. For more information please see section 2 of the 1st and 3.2.3 of the 2nd: Andrew W.
class	TopDownConstructor The class implementing the TopDown construction method of ball trees.

Uses of RevisionHandler in weka.core.neighboursearch.covertrees

Classes in weka.core.neighboursearch.covertrees that implement RevisionHandler

class

Stack<T> Class implementing a stack.

Uses of RevisionHandler in weka.core.neighboursearch.kdtrees

Classes in weka.core.neighboursearch.kdtrees that implement RevisionHandler

class	KDTreeNode A class representing a KDTree node.
class	KDTreeNodeSplitter Class that splits up a KDTreeNode.
class	KMeansInpiredMethod The class that splits a node into two such that the overall sum of squared distances of points to their centres on both sides of the (axis-parallel) splitting plane is minimum. For more information see also: Ashraf Masood Kibriya (2007).
class	MedianOfWidestDimension The class that splits a KDTree node based on the median value of a dimension in which the node's points have the widest spread. For more information see also: Jerome H.
class	MidPointOfWidestDimension The class that splits a KDTree node based on the midpoint value of a dimension in which the node's points have the widest spread. For more information see also: Andrew Moore (1991).
class	SlidingMidPointOfWidestSide The class that splits a node into two based on the midpoint value of the dimension in which the node's rectangle is widest.

Uses of RevisionHandler in weka.core.stemmers

Subinterfaces of RevisionHandler in weka.core.stemmers

interface

Stemmer Interface for all stemming algorithms.

Classes in weka.core.stemmers that implement RevisionHandler

class	IteratedLovinsStemmer An iterated version of the Lovins stemmer.
class	LovinsStemmer A stemmer based on the Lovins stemmer, described here: Julie Beth Lovins (1968).
class	NullStemmer A dummy stemmer that performs no stemming at all.
class	SnowballStemmer A wrapper class for the Snowball stemmers.
class	Stemming A helper class for using the stemmers.

Uses of RevisionHandler in weka.core.tokenizers

Classes in weka.core.tokenizers that implement RevisionHandler

class	AlphabeticTokenizer Alphabetic string tokenizer, tokens are to be formed only from contiguous alphabetic sequences.
class	CharacterDelimitedTokenizer Abstract superclass for tokenizers that take characters as delimiters.
class	NGramTokenizer Splits a string into an n-gram with min and max grams.
class	Tokenizer A superclass for all tokenizer algorithms.
class	WordTokenizer A simple tokenizer that is using the java.util.StringTokenizer class to tokenize the strings.

Uses of RevisionHandler in weka.core.xml

Classes in weka.core.xml that implement RevisionHandler

class	KOML This class is a helper class for XML serialization using KOML .
class	MethodHandler This class handles relationships between display names of properties (or classes) and Methods that are associated with them.
class	PropertyHandler This class stores information about properties to ignore or properties that are allowed for a certain class.
class	SerialUIDChanger This class enables one to change the UID of a serialized object and therefore not losing the data stored in the binary format.
class	XMLBasicSerialization This serializer contains some read/write methods for common classes that are not beans-conform.
class	XMLDocument This class offers some methods for generating, reading and writing XML documents. It can only handle UTF-8.
class	XMLInstances XML representation of the Instances class.
class	XMLOptions A class for transforming options listed in XML to a regular WEKA command line string.
class	XMLSerialization With this class objects can be serialized to XML instead into a binary format.
class	XMLSerializationMethodHandler This class handles relationships between display names of properties (or classes) and Methods that are associated with them.
class	XStream This class is a helper class for XML serialization using XStream .

Uses of RevisionHandler in weka.datagenerators

Classes in weka.datagenerators that implement RevisionHandler

class	ClassificationGenerator Abstract class for data generators for classifiers.
class	ClusterDefinition Ancestor to all ClusterDefinitions, i.e., subclasses that handle their own parameters that the cluster generator only passes on.
class	ClusterGenerator Abstract class for cluster data generators.
class	DataGenerator Abstract superclass for data generators that generate data for classifiers and clusterers.
class	RegressionGenerator Abstract class for data generators for regression classifiers.
class	Test Class to represent a test.

Uses of RevisionHandler in weka.datagenerators.classifiers.classification

Classes in weka.datagenerators.classifiers.classification that implement RevisionHandler

class	Agrawal Generates a people database and is based on the paper by Agrawal et al.: R.
class	LED24 This generator produces data for a display with 7 LEDs.
class	RandomRBF RandomRBF data is generated by first creating a random set of centers for each class.
class	RDG1 A data generator that produces data randomly by producing a decision list. The decision list consists of rules. Instances are generated randomly one by one.

Uses of RevisionHandler in weka.datagenerators.classifiers.regression

Classes in weka.datagenerators.classifiers.regression that implement RevisionHandler

class	Expression A data generator for generating y according to a given expression out of randomly generated x. E.g., the mexican hat can be generated like this: sin(abs(a1)) / abs(a1) In addition to this function, the amplitude can be changed and gaussian noise can be added.
class	MexicanHat A data generator for the simple 'Mexian Hat' function: y = sin|x| / |x| In addition to this simple function, the amplitude can be changed and gaussian noise can be added.

Uses of RevisionHandler in weka.datagenerators.clusterers

Classes in weka.datagenerators.clusterers that implement RevisionHandler

class	BIRCHCluster Cluster data generator designed for the BIRCH System Dataset is generated with instances in K clusters. Instances are 2-d data points. Each cluster is characterized by the number of data points in itits radius and its center.
class	SubspaceCluster A data generator that produces data points in hyperrectangular subspace clusters.
class	SubspaceClusterDefinition A single cluster for the SubspaceCluster datagenerator Valid options are:

Uses of RevisionHandler in weka.estimators

Subinterfaces of RevisionHandler in weka.estimators

interface

ConditionalEstimator Interface for conditional probability estimators.

Classes in weka.estimators that implement RevisionHandler

class	CheckEstimator Class for examining the capabilities and finding problems with estimators.
static class	CheckEstimator.AttrTypes class that contains info about the attribute types the estimator can estimate estimator work on one attribute only
static class	CheckEstimator.EstTypes public class that contains info about the chosen attribute type estimator work on one attribute only
class	CheckEstimator.PostProcessor a class for postprocessing the test-data
class	DDConditionalEstimator Conditional probability estimator for a discrete domain conditional upon a discrete domain.
class	DiscreteEstimator Simple symbolic probability estimator based on symbol counts.
class	DKConditionalEstimator Conditional probability estimator for a discrete domain conditional upon a numeric domain.
class	DNConditionalEstimator Conditional probability estimator for a discrete domain conditional upon a numeric domain.
class	Estimator Abstract class for all estimators.
class	EstimatorUtils Contains static utility functions for Estimators.
class	KDConditionalEstimator Conditional probability estimator for a numeric domain conditional upon a discrete domain (utilises separate kernel estimators for each discrete conditioning value).
class	KernelEstimator Simple kernel density estimator.
class	KKConditionalEstimator Conditional probability estimator for a numeric domain conditional upon a numeric domain.
class	MahalanobisEstimator Simple probability estimator that places a single normal distribution over the observed values.
class	NDConditionalEstimator Conditional probability estimator for a numeric domain conditional upon a discrete domain (utilises separate normal estimators for each discrete conditioning value).
class	NNConditionalEstimator Conditional probability estimator for a numeric domain conditional upon a numeric domain (using Mahalanobis distance).
class	NormalEstimator Simple probability estimator that places a single normal distribution over the observed values.
class	PoissonEstimator Simple probability estimator that places a single Poisson distribution over the observed values.

Uses of RevisionHandler in weka.experiment

Classes in weka.experiment that implement RevisionHandler

class	AveragingResultProducer Takes the results from a ResultProducer and submits the average to the result listener.
class	ClassifierSplitEvaluator A SplitEvaluator that produces results for a classification scheme on a nominal class attribute.
class	CostSensitiveClassifierSplitEvaluator SplitEvaluator that produces results for a classification scheme on a nominal class attribute, including weighted misclassification costs.
class	CrossValidationResultProducer Generates for each run, carries out an n-fold cross-validation, using the set SplitEvaluator to generate some results.
class	CSVResultListener Takes results from a result producer and assembles them into comma separated value form.
class	DatabaseResultListener Takes results from a result producer and sends them to a database.
class	DatabaseResultProducer Examines a database and extracts out the results produced by the specified ResultProducer and submits them to the specified ResultListener.
class	DatabaseUtils DatabaseUtils provides utility functions for accessing the experiment database.
class	DensityBasedClustererSplitEvaluator A SplitEvaluator that produces results for a density based clusterer.
class	Experiment Holds all the necessary configuration information for a standard type experiment.
class	InstanceQuery Convert the results of a database query into instances.
class	InstancesResultListener Outputs the received results in arff format to a Writer.
class	LearningRateResultProducer Tells a sub-ResultProducer to reproduce the current run for varying sized subsamples of the dataset.
class	OutputZipper OutputZipper writes output to either gzipped files or to a multi entry zip file.
class	PairedCorrectedTTester Behaves the same as PairedTTester, only it uses the corrected resampled t-test statistic. For more information see: Claude Nadeau, Yoshua Bengio (2001).
class	PairedStats A class for storing stats on a paired comparison (t-test and correlation)
class	PairedStatsCorrected A class for storing stats on a paired comparison.
class	PairedTTester Calculates T-Test statistics on data stored in a set of instances.
class	PropertyNode Stores information on a property of an object: the class of the object with the property; the property descriptor, and the current value.
class	RandomSplitResultProducer Generates a single train/test split and calls the appropriate SplitEvaluator to generate some results.
class	RegressionSplitEvaluator A SplitEvaluator that produces results for a classification scheme on a numeric class attribute.
class	RemoteEngine A general purpose server for executing Task objects sent via RMI.
class	RemoteExperiment Holds all the necessary configuration information for a distributed experiment.
class	RemoteExperimentSubTask Class to encapsulate an experiment as a task that can be executed on a remote host.
class	ResultMatrix This matrix is a container for the datasets and classifier setups and their statistics.
class	ResultMatrixCSV This matrix is a container for the datasets and classifier setups and their statistics.
class	ResultMatrixGnuPlot This matrix is a container for the datasets and classifier setups and their statistics.
class	ResultMatrixHTML This matrix is a container for the datasets and classifier setups and their statistics.
class	ResultMatrixLatex This matrix is a container for the datasets and classifier setups and their statistics.
class	ResultMatrixPlainText This matrix is a container for the datasets and classifier setups and their statistics.
class	ResultMatrixSignificance This matrix is a container for the datasets and classifier setups and their statistics.
class	TaskStatusInfo A class holding information for tasks being executed on RemoteEngines.

Uses of RevisionHandler in weka.experiment.xml

Classes in weka.experiment.xml that implement RevisionHandler

class

XMLExperiment This class serializes and deserializes an Experiment instance to and fro XML. It omits the options from the Experiment, since these are handled by the get/set-methods.

Uses of RevisionHandler in weka.filters

Classes in weka.filters that implement RevisionHandler

class	AllFilter A simple instance filter that passes all instances directly through.
class	Filter An abstract class for instance filters: objects that take instances as input, carry out some transformation on the instance and then output the instance.
class	MultiFilter Applies several filters successively.
class	SimpleBatchFilter This filter is a superclass for simple batch filters.
class	SimpleFilter This filter contains common behavior of the SimpleBatchFilter and the SimpleStreamFilter.
class	SimpleStreamFilter This filter is a superclass for simple stream filters.

Uses of RevisionHandler in weka.filters.supervised.attribute

Classes in weka.filters.supervised.attribute that implement RevisionHandler

class	AddClassification A filter for adding the classification, the class distribution and an error flag to a dataset with a classifier.
class	AttributeSelection A supervised attribute filter that can be used to select attributes.
class	ClassOrder Changes the order of the classes so that the class values are no longer of in the order specified in the header.
class	Discretize An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
class	NominalToBinary Converts all nominal attributes into binary numeric attributes.
class	PLSFilter Runs Partial Least Square Regression over the given instances and computes the resulting beta matrix for prediction. By default it replaces missing values and centers the data. For more information see: Tormod Naes, Tomas Isaksson, Tom Fearn, Tony Davies (2002).

Uses of RevisionHandler in weka.filters.supervised.instance

Classes in weka.filters.supervised.instance that implement RevisionHandler

class	Resample Produces a random subsample of a dataset using either sampling with replacement or without replacement. The original dataset must fit entirely in memory.
class	SMOTE Resamples a dataset by applying the Synthetic Minority Oversampling TEchnique (SMOTE).
class	SpreadSubsample Produces a random subsample of a dataset.
class	StratifiedRemoveFolds This filter takes a dataset and outputs a specified fold for cross validation.

Uses of RevisionHandler in weka.filters.unsupervised.attribute

Classes in weka.filters.unsupervised.attribute that implement RevisionHandler

class	AbstractTimeSeries An abstract instance filter that assumes instances form time-series data and performs some merging of attribute values in the current instance with attribute attribute values of some previous (or future) instance.
class	Add An instance filter that adds a new attribute to the dataset.
class	AddCluster A filter that adds a new nominal attribute representing the cluster assigned to each instance by the specified clustering algorithm.
class	AddExpression An instance filter that creates a new attribute by applying a mathematical expression to existing attributes.
class	AddID An instance filter that adds an ID attribute to the dataset.
class	AddNoise An instance filter that changes a percentage of a given attributes values.
class	AddValues Adds the labels from the given list to an attribute if they are missing.
class	Center Centers all numeric attributes in the given dataset to have zero mean (apart from the class attribute, if set).
class	ChangeDateFormat Changes the date format used by a date attribute.
class	ClassAssigner Filter that can set and unset the class index.
class	ClusterMembership A filter that uses a density-based clusterer to generate cluster membership values; filtered instances are composed of these values plus the class attribute (if set in the input data).
class	Copy An instance filter that copies a range of attributes in the dataset.
class	FirstOrder This instance filter takes a range of N numeric attributes and replaces them with N-1 numeric attributes, the values of which are the difference between consecutive attribute values from the original instance.
class	InterquartileRange A filter for detecting outliers and extreme values based on interquartile ranges.
class	KernelFilter Converts the given set of predictor variables into a kernel matrix.
class	MakeIndicator A filter that creates a new dataset with a boolean attribute replacing a nominal attribute.
class	MathExpression Modify numeric attributes according to a given expression Valid options are:
class	MergeTwoValues Merges two values of a nominal attribute into one value.
class	MultiInstanceToPropositional Converts the multi-instance dataset into single instance dataset so that the Nominalize, Standardize and other type of filters or transformation can be applied to these data for the further preprocessing. Note: the first attribute of the converted dataset is a nominal attribute and refers to the bagId.
class	NominalToString Converts a nominal attribute (i.e.
class	NumericCleaner A filter that 'cleanses' the numeric data from values that are too small, too big or very close to a certain value (e.g., 0) and sets these values to a pre-defined default.
class	NumericToBinary Converts all numeric attributes into binary attributes (apart from the class attribute, if set): if the value of the numeric attribute is exactly zero, the value of the new attribute will be zero.
class	NumericToNominal A filter for turning numeric attributes into nominal ones.
class	NumericTransform Transforms numeric attributes using a given transformation method.
class	Obfuscate A simple instance filter that renames the relation, all attribute names and all nominal (and string) attribute values.
class	PartitionedMultiFilter A filter that applies filters on subsets of attributes and assembles the output into a new dataset.
class	PKIDiscretize Discretizes numeric attributes using equal frequency binning, where the number of bins is equal to the square root of the number of non-missing values. For more information, see: Ying Yang, Geoffrey I.
class	PotentialClassIgnorer This filter should be extended by other unsupervised attribute filters to allow processing of the class attribute if that's required.
class	PrincipalComponents Performs a principal components analysis and transformation of the data. Dimensionality reduction is accomplished by choosing enough eigenvectors to account for some percentage of the variance in the original data -- default 0.95 (95%). Based on code of the attribute selection scheme 'PrincipalComponents' by Mark Hall and Gabi Schmidberger.
class	PropositionalToMultiInstance Converts the propositional instance dataset into multi-instance dataset (with relational attribute).
class	RandomProjection Reduces the dimensionality of the data by projecting it onto a lower dimensional subspace using a random matrix with columns of unit length (i.e.
class	RandomSubset Chooses a random subset of attributes, either an absolute number or a percentage.
class	RELAGGS A propositionalization filter inspired by the RELAGGS algorithm. It processes all relational attributes that fall into the user defined range (all others are skipped, i.e., not added to the output).
class	Remove A filter that removes a range of attributes from the dataset.
class	RemoveType Removes attributes of a given type.
class	RemoveUseless This filter removes attributes that do not vary at all or that vary too much.
class	Reorder A filter that generates output with a new order of the attributes.
class	ReplaceMissingValues Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.
class	Standardize Standardizes all numeric attributes in the given dataset to have zero mean and unit variance (apart from the class attribute, if set).
class	StringToNominal Converts a string attribute (i.e.
class	StringToWordVector Converts String attributes into a set of attributes representing word occurrence (depending on the tokenizer) information from the text contained in the strings.
class	SwapValues Swaps two values of a nominal attribute.
class	TimeSeriesDelta An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the difference between the current value and the equivalent attribute attribute value of some previous (or future) instance.
class	TimeSeriesTranslate An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the equivalent attribute values of some previous (or future) instance.
class	Wavelet A filter for wavelet transformation. For more information see: Wikipedia (2004).

Uses of RevisionHandler in weka.filters.unsupervised.instance

Classes in weka.filters.unsupervised.instance that implement RevisionHandler

class	NonSparseToSparse An instance filter that converts all incoming instances into sparse format.
class	Normalize An instance filter that normalize instances considering only numeric attributes and ignoring class index.
class	Randomize Randomly shuffles the order of instances passed through it.
class	RemoveFolds This filter takes a dataset and outputs a specified fold for cross validation.
class	RemoveFrequentValues Determines which values (frequent or infrequent ones) of an (nominal) attribute are retained and filters the instances accordingly.
class	RemoveMisclassified A filter that removes instances which are incorrectly classified.
class	RemovePercentage A filter that removes a given percentage of a dataset.
class	RemoveRange A filter that removes a given range of instances of a dataset.
class	RemoveWithValues Filters instances according to the value of an attribute.
class	ReservoirSample Produces a random subsample of a dataset using the reservoir sampling Algorithm "R" by Vitter.
class	SparseToNonSparse An instance filter that converts all incoming sparse instances into non-sparse format.
class	SubsetByExpression Filters instances according to a user-specified expression. Grammar: boolexpr_list ::= boolexpr_list boolexpr_part | boolexpr_part; boolexpr_part ::= boolexpr:e {: parser.setResult(e); :} ; boolexpr ::= BOOLEAN | true | false | expr < expr | expr <= expr | expr > expr | expr >= expr | expr = expr | ( boolexpr ) | not boolexpr | boolexpr and boolexpr | boolexpr or boolexpr | ATTRIBUTE is STRING ; expr ::= NUMBER | ATTRIBUTE | ( expr ) | opexpr | funcexpr ; opexpr ::= expr + expr | expr - expr | expr * expr | expr / expr ; funcexpr ::= abs ( expr ) | sqrt ( expr ) | log ( expr ) | exp ( expr ) | sin ( expr ) | cos ( expr ) | tan ( expr ) | rint ( expr ) | floor ( expr ) | pow ( expr for base , expr for exponent ) | ceil ( expr ) ; Notes: - NUMBER any integer or floating point number (but not in scientific notation!) - STRING any string surrounded by single quotes; the string may not contain a single quote though. - ATTRIBUTE the following placeholders are recognized for attribute values: - CLASS for the class value in case a class attribute is set. - ATTxyz with xyz a number from 1 to # of attributes in the dataset, representing the value of indexed attribute. Examples: - extracting only mammals and birds from the 'zoo' UCI dataset: (CLASS is 'mammal') or (CLASS is 'bird') - extracting only animals with at least 2 legs from the 'zoo' UCI dataset: (ATT14 >= 2) - extracting only instances with non-missing 'wage-increase-second-year' from the 'labor' UCI dataset: not ismissing(ATT3) Valid options are:

Uses of RevisionHandler in weka.gui.beans

Classes in weka.gui.beans that implement RevisionHandler

class

FlowRunner Small utility class for executing KnowledgeFlow flows outside of the KnowledgeFlow application

Uses of RevisionHandler in weka.gui.beans.xml

Classes in weka.gui.beans.xml that implement RevisionHandler

class

XMLBeans This class serializes and deserializes a KnowledgeFlow setup to and fro XML.

Uses of RevisionHandler in weka.gui.sql

Classes in weka.gui.sql that implement RevisionHandler

class

DbUtils A little bit extended DatabaseUtils class.