Uses of Interface
weka.core.CapabilitiesHandler

Packages that use CapabilitiesHandler

weka.associations
weka.attributeSelection
weka.classifiers
weka.classifiers.bayes
weka.classifiers.bayes.net
weka.classifiers.bayes.net.estimate
weka.classifiers.functions
weka.classifiers.functions.supportVector
weka.classifiers.lazy
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.ft
weka.classifiers.trees.j48
weka.classifiers.trees.lmt
weka.classifiers.trees.m5
weka.clusterers
weka.core
weka.core.converters
weka.estimators
weka.filters
weka.filters.supervised.attribute
weka.filters.supervised.instance
weka.filters.unsupervised.attribute
weka.filters.unsupervised.instance

Uses of CapabilitiesHandler in weka.associations

Classes in weka.associations that implement CapabilitiesHandler

class	AbstractAssociator Abstract scheme for learning associations.
class	Apriori Class implementing an Apriori-type algorithm.
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	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	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 CapabilitiesHandler in weka.attributeSelection

Classes in weka.attributeSelection that implement CapabilitiesHandler

class	ASEvaluation Abstract attribute selection evaluation class
class	AttributeSetEvaluator Abstract attribute set evaluator.
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	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	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	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	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	OneRAttributeEval OneRAttributeEval : Evaluates the worth of an attribute by using the OneR classifier. Valid options are:
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	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 CapabilitiesHandler in weka.classifiers

Classes in weka.classifiers that implement CapabilitiesHandler

class	Classifier Abstract classifier.
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 CapabilitiesHandler in weka.classifiers.bayes

Classes in weka.classifiers.bayes that implement CapabilitiesHandler

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 CapabilitiesHandler in weka.classifiers.bayes.net

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

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.

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

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

class	DiscreteEstimatorBayes Symbolic probability estimator based on symbol counts and a prior.
class	DiscreteEstimatorFullBayes Symbolic probability estimator based on symbol counts and a prior.

Uses of CapabilitiesHandler in weka.classifiers.functions

Classes in weka.classifiers.functions that implement CapabilitiesHandler

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 CapabilitiesHandler in weka.classifiers.functions.supportVector

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

class	CachedKernel Base class for RBFKernel and PolyKernel that implements a simple LRU.
class	Kernel Abstract kernel.
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	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 CapabilitiesHandler in weka.classifiers.lazy

Classes in weka.classifiers.lazy that implement CapabilitiesHandler

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	LWL Locally weighted learning.

Uses of CapabilitiesHandler in weka.classifiers.meta

Classes in weka.classifiers.meta that implement CapabilitiesHandler

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 CapabilitiesHandler in weka.classifiers.meta.nestedDichotomies

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

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 CapabilitiesHandler in weka.classifiers.mi

Classes in weka.classifiers.mi that implement CapabilitiesHandler

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 CapabilitiesHandler in weka.classifiers.mi.supportVector

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

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 CapabilitiesHandler in weka.classifiers.misc

Classes in weka.classifiers.misc that implement CapabilitiesHandler

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 CapabilitiesHandler in weka.classifiers.pmml.consumer

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

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 CapabilitiesHandler in weka.classifiers.rules

Classes in weka.classifiers.rules that implement CapabilitiesHandler

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	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	ZeroR Class for building and using a 0-R classifier.

Uses of CapabilitiesHandler in weka.classifiers.rules.part

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

class

MakeDecList Class for handling a decision list.

Uses of CapabilitiesHandler in weka.classifiers.trees

Classes in weka.classifiers.trees that implement CapabilitiesHandler

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 CapabilitiesHandler in weka.classifiers.trees.ft

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

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 CapabilitiesHandler in weka.classifiers.trees.j48

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

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	ClassifierTree Class for handling a tree structure used for classification.
class	NBTreeClassifierTree Class for handling a naive bayes tree structure used for classification.
class	PruneableClassifierTree Class for handling a tree structure that can be pruned using a pruning set.

Uses of CapabilitiesHandler in weka.classifiers.trees.lmt

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

class	LMTNode Class for logistic model tree structure.
class	LogisticBase Base/helper class for building logistic regression models with the LogitBoost algorithm.

Uses of CapabilitiesHandler in weka.classifiers.trees.m5

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

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

Uses of CapabilitiesHandler in weka.clusterers

Classes in weka.clusterers that implement CapabilitiesHandler

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	CLOPE Yiling Yang, Xudong Guan, Jinyuan You: CLOPE: a fast and effective clustering algorithm for transactional data.
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 CapabilitiesHandler in weka.core

Subinterfaces of CapabilitiesHandler in weka.core

interface

MultiInstanceCapabilitiesHandler Multi-Instance classifiers can specify an additional Capabilities object for the data in the relational attribute, since the format of multi-instance data is fixed to "bag/NOMINAL,data/RELATIONAL,class".

Classes in weka.core that implement CapabilitiesHandler

class

FindWithCapabilities Locates all classes with certain capabilities.

Methods in weka.core that return CapabilitiesHandler

CapabilitiesHandler	TestInstances.getHandler() returns the current set CapabilitiesHandler to generate the dataset for, can be null
CapabilitiesHandler	FindWithCapabilities.getHandler() returns the current set CapabilitiesHandler to generate the dataset for, can be null.
CapabilitiesHandler	Capabilities.getOwner() returns the owner of this capabilities object

Methods in weka.core with parameters of type CapabilitiesHandler

void	TestInstances.setHandler(CapabilitiesHandler value) sets the Capabilities handler to generate the data for
void	FindWithCapabilities.setHandler(CapabilitiesHandler value) sets the Capabilities handler to generate the data for.
void	Capabilities.setOwner(CapabilitiesHandler value) sets the owner of this capabilities object

Constructors in weka.core with parameters of type CapabilitiesHandler

Capabilities(CapabilitiesHandler owner)
initializes the capabilities for the given owner

Uses of CapabilitiesHandler in weka.core.converters

Classes in weka.core.converters that implement CapabilitiesHandler

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	AbstractSaver Abstract class for Saver
class	ArffSaver Writes to a destination in arff text 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	CSVSaver Writes to a destination that is in csv format Valid options are:
class	DatabaseSaver Writes to a database (tested with MySQL, InstantDB, HSQLDB).
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	SerializedInstancesSaver Serializes the instances to a file with extension bsi.
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	XRFFSaver Writes to a destination that is in the XML version of the ARFF format.

Uses of CapabilitiesHandler in weka.estimators

Classes in weka.estimators that implement CapabilitiesHandler

class	DiscreteEstimator Simple symbolic probability estimator based on symbol counts.
class	Estimator Abstract class for all estimators.
class	KernelEstimator Simple kernel density estimator.
class	MahalanobisEstimator Simple probability estimator that places a single normal distribution over the observed values.
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 CapabilitiesHandler in weka.filters

Classes in weka.filters that implement CapabilitiesHandler

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 CapabilitiesHandler in weka.filters.supervised.attribute

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

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 CapabilitiesHandler in weka.filters.supervised.instance

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

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 CapabilitiesHandler in weka.filters.unsupervised.attribute

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

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 CapabilitiesHandler in weka.filters.unsupervised.instance

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

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: