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Uses of Interface
weka.core.RevisionHandler

Packages that use RevisionHandler
weka.associations   
weka.attributeSelection   
weka.classifiers   
weka.classifiers.bayes   
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.supportVector   
weka.classifiers.lazy   
weka.classifiers.lazy.kstar   
weka.classifiers.meta   
weka.classifiers.misc   
weka.classifiers.pmml.consumer   
weka.classifiers.rules   
weka.classifiers.rules.part   
weka.classifiers.trees   
weka.classifiers.trees.j48   
weka.classifiers.trees.lmt   
weka.classifiers.trees.m5   
weka.classifiers.xml   
weka.clusterers   
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.scripting   
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 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 ItemSet
          Class for storing a set of items.
 class LabeledItemSet
          Class for storing a set of items together with a class label.
 class SingleAssociatorEnhancer
          Abstract utility class for handling settings common to meta associators that use a single base associator.
 

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 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 OneRAttributeEval
          OneRAttributeEval :

Evaluates the worth of an attribute by using the OneR classifier.

Valid options are:

 class Ranker
          Ranker :

Ranks attributes by their individual evaluations.
 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 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 AbstractClassifier
          Abstract classifier.
 class AggregateableEvaluation
          Subclass of Evaluation that provides a method for aggregating the results stored in another Evaluation object.
 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 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 ParallelIteratedSingleClassifierEnhancer
          Abstract utility class for handling settings common to meta classifiers that build an ensemble in parallel from a single base learner.
 class ParallelMultipleClassifiersCombiner
          Abstract utility class for handling settings common to meta classifiers that build an ensemble in parallel using 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 RandomizableParallelIteratedSingleClassifierEnhancer
          Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble in parallel from a single base learner.
 class RandomizableParallelMultipleClassifiersCombiner
          Abstract utility class for handling settings common to meta classifiers that build an ensemble in parallel using 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 BayesNet
          Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier.
 class NaiveBayes
          Class for a Naive Bayes classifier using estimator classes.
 class NaiveBayesMultinomial
          Class for building and using a multinomial Naive Bayes classifier.
 class NaiveBayesMultinomialText
          Multinomial naive bayes for text data.
 class NaiveBayesMultinomialUpdateable
          Class for building and using a multinomial Naive Bayes classifier.
 class NaiveBayesUpdateable
          Class for a Naive Bayes classifier using estimator classes.
 

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 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 SGD
          Implements stochastic gradient descent for learning various linear models (binary class SVM, binary class logistic regression and linear regression).
 class SGDText
          Implements stochastic gradient descent for learning a linear binary class SVM or binary class logistic regression on text data.
 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 VotedPerceptron
          Implementation of the voted perceptron algorithm by Freund and Schapire.
 

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.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 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 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 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 FilteredClassifier
          Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
 class LogitBoost
          Class for performing additive logistic regression.
 class MultiClassClassifier
          A metaclassifier for handling multi-class datasets with 2-class classifiers.
 class MultiClassClassifierUpdateable
          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 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 Stacking
          Combines several classifiers using the stacking method.
 class Vote
          Class for combining classifiers.
 

Uses of RevisionHandler in weka.classifiers.misc
 

Classes in weka.classifiers.misc that implement RevisionHandler
 class InputMappedClassifier
          Wrapper classifier that addresses incompatible training and test data by building a mapping between the training data that a classifier has been built with and the incoming test instances' structure.
 class SerializedClassifier
          A wrapper around a serialized classifier model.
 

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.
 class RuleSetModel
          Class implementing import of PMML RuleSetModel.
 class SupportVectorMachineModel
          Implements a PMML SupportVectorMachineModel
 class TreeModel
          Class implementing import of PMML TreeModel.
 

Uses of RevisionHandler in weka.classifiers.rules
 

Classes in weka.classifiers.rules that implement RevisionHandler
 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 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 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 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 DecisionStump
          Class for building and using a decision stump.
 class J48
          Class for generating a pruned or unpruned C4.5 decision tree.
 class LMT
          Classifier for building 'logistic model trees', which are classification trees with logistic regression functions at the leaves.
 class M5P
          M5Base.
 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.
 

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 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 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 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 Cobweb.CNode
          Inner class handling node operations for Cobweb.
 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 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 SimpleKMeans
          Cluster data using the k means algorithm.
 class SingleClustererEnhancer
          Meta-clusterer for enhancing a base clusterer.
 

Uses of RevisionHandler in weka.core
 

Classes in weka.core that implement RevisionHandler
 class AbstractInstance
          Abstract class providing common functionality for the original instance implementations.
 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 ClassCache
          A singleton that stores all classes on the classpath.
 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 DenseInstance
          Class for handling an instance.
 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<E>
          Deprecated. 
 class FindWithCapabilities
          Locates all classes with certain capabilities.
 class GlobalInfoJavadoc
          Generates Javadoc comments from the class's globalInfo method.
 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 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 Matrix
          Deprecated. Use weka.core.matrix.Matrix instead - only for backwards compatibility.
 class Memory
          A little helper class for Memory management.
 class MinkowskiDistance
          Implementing Minkowski 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 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.
 class WekaEnumeration
          Class for enumerating an array list's elements.
 

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 format (the default).
 class CSVSaver
          Writes to a destination that is in CSV (comma-separated values) format.
 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 JSONLoader
          Reads a source that is in the JSON format.
It automatically decompresses the data if the extension is '.json.gz'.

For more information, see JSON homepage:
http://www.json.org/

 class JSONSaver
          Writes to a destination that is in JSON format.
The data can be compressed with gzip, in order to save space.

For more information, see JSON homepage:
http://www.json.org/

Valid options are:

 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 MatlabLoader
          Reads a Matlab file containing a single matrix in ASCII format.
 class MatlabSaver
          Writes Matlab ASCII files, in single or double precision format.
 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 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.scripting
 

Classes in weka.core.scripting that implement RevisionHandler
 class Groovy
          A helper class for Groovy.
 class Jython
          A helper class for Jython.
 

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 CrossValidationSplitResultProducer
          Carries out one split of a repeated k-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 ExplicitTestsetResultProducer
          Loads the external test set and calls the appropriate SplitEvaluator to generate some results.
The filename of the test set is constructed as follows:
<dir> + / + <prefix> + <relation-name> + <suffix>
The relation-name can be modified by using the regular expression to replace the matching sub-string with a specified replacement string.
 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
          Generates the matrix in CSV ('comma-separated values') format.
 class ResultMatrixGnuPlot
          Generates output for a data and script file for GnuPlot.
 class ResultMatrixHTML
          Generates the matrix output as HTML.
 class ResultMatrixLatex
          Generates the matrix output in LaTeX-syntax.
 class ResultMatrixPlainText
          Generates the output as plain text (for fixed width fonts).
 class ResultMatrixSignificance
          Only outputs the significance indicators.
 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.
 

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 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.
Either the clustering algorithm gets built with the first batch of data or one specifies are serialized clusterer model file to use instead.
 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 MergeManyValues
          Merges many values of a nominal attribute into one value.
 class MergeTwoValues
          Merges two values of a nominal attribute into one value.
 class NominalToString
          Converts a nominal attribute (i.e.
 class Normalize
          Normalizes all numeric values in the given dataset (apart from the class attribute, if set).
 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 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 Remove
          An filter that removes a range of attributes from the dataset.
 class RemoveByName
          Removes attributes based on a regular expression matched against their names.
 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 RenameAttribute
          This filter is used for renaming attribute names.
Regular expressions can be used in the matching and replacing.
See Javadoc of java.util.regex.Pattern class for more information:
http://java.sun.com/javase/6/docs/api/java/util/regex/Pattern.html

Valid options are:

 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 SortLabels
          A simple filter for sorting the labels of nominal attributes.
 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.
 

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 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.
 

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