Uses of Interface
weka.core.OptionHandler

Packages that use OptionHandler

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.output.prediction
weka.classifiers.functions
weka.classifiers.functions.supportVector
weka.classifiers.lazy
weka.classifiers.meta
weka.classifiers.misc
weka.classifiers.pmml.consumer
weka.classifiers.rules
weka.classifiers.trees
weka.classifiers.trees.lmt
weka.classifiers.trees.m5
weka.clusterers
weka.core
weka.core.converters
weka.core.neighboursearch
weka.core.neighboursearch.balltrees
weka.core.neighboursearch.kdtrees
weka.core.stemmers
weka.core.tokenizers
weka.datagenerators
weka.datagenerators.classifiers.classification
weka.datagenerators.classifiers.regression
weka.datagenerators.clusterers
weka.estimators
weka.experiment
weka.filters
weka.filters.supervised.attribute
weka.filters.supervised.instance
weka.filters.unsupervised.attribute
weka.filters.unsupervised.instance
weka.gui
weka.gui.explorer
weka.gui.scripting

Uses of OptionHandler in weka.associations

Subinterfaces of OptionHandler in weka.associations

interface

CARuleMiner Interface for learning class association rules.

Classes in weka.associations that implement OptionHandler

class	Apriori Class implementing an Apriori-type algorithm.
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	SingleAssociatorEnhancer Abstract utility class for handling settings common to meta associators that use a single base associator.

Uses of OptionHandler in weka.attributeSelection

Classes in weka.attributeSelection that implement OptionHandler

class	BestFirst BestFirst: Searches the space of attribute subsets by greedy hillclimbing augmented with a backtracking facility.
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	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	WrapperSubsetEval WrapperSubsetEval: Evaluates attribute sets by using a learning scheme.

Uses of OptionHandler in weka.classifiers

Classes in weka.classifiers that implement OptionHandler

class	AbstractClassifier Abstract classifier.
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	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 OptionHandler in weka.classifiers.bayes

Classes in weka.classifiers.bayes that implement OptionHandler

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 OptionHandler in weka.classifiers.bayes.net

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

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 OptionHandler in weka.classifiers.bayes.net.estimate

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

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 OptionHandler in weka.classifiers.bayes.net.search

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

class

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

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

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

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 OptionHandler in weka.classifiers.bayes.net.search.fixed

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

class

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

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

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

class

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

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

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

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 OptionHandler in weka.classifiers.evaluation.output.prediction

Classes in weka.classifiers.evaluation.output.prediction that implement OptionHandler

class	AbstractOutput A superclass for outputting the classifications of a classifier.
class	CSV Outputs the predictions as CSV.
class	HTML Outputs the predictions in HTML.
class	Null Suppresses all output.
class	PlainText Outputs the predictions in plain text.
class	XML Outputs the predictions in XML. The following DTD is used: <!DOCTYPE predictions [ <!ELEMENT predictions (prediction*)> <!ATTLIST predictions version CDATA "3.5.8"> <!ATTLIST predictions name CDATA #REQUIRED> <!ELEMENT prediction ((actual_label,predicted_label,error,(prediction|distribution),attributes?)|(actual_value,predicted_value,error,attributes?))> <!ATTLIST prediction index CDATA #REQUIRED> <!ELEMENT actual_label ANY> <!ATTLIST actual_label index CDATA #REQUIRED> <!ELEMENT predicted_label ANY> <!ATTLIST predicted_label index CDATA #REQUIRED> <!ELEMENT error ANY> <!ELEMENT prediction ANY> <!ELEMENT distribution (class_label+)> <!ELEMENT class_label ANY> <!ATTLIST class_label index CDATA #REQUIRED> <!ATTLIST class_label predicted (yes|no) "no"> <!ELEMENT actual_value ANY> <!ELEMENT predicted_value ANY> <!ELEMENT attributes (attribute+)> <!ELEMENT attribute ANY> <!ATTLIST attribute index CDATA #REQUIRED> <!ATTLIST attribute name CDATA #REQUIRED> <!ATTLIST attribute type (numeric|date|nominal|string|relational) #REQUIRED> ] > Valid options are:

Uses of OptionHandler in weka.classifiers.functions

Classes in weka.classifiers.functions that implement OptionHandler

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 OptionHandler in weka.classifiers.functions.supportVector

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

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	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	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 OptionHandler in weka.classifiers.lazy

Classes in weka.classifiers.lazy that implement OptionHandler

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 OptionHandler in weka.classifiers.meta

Classes in weka.classifiers.meta that implement OptionHandler

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 OptionHandler in weka.classifiers.misc

Classes in weka.classifiers.misc that implement OptionHandler

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 OptionHandler in weka.classifiers.pmml.consumer

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

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 OptionHandler in weka.classifiers.rules

Classes in weka.classifiers.rules that implement OptionHandler

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

Uses of OptionHandler in weka.classifiers.trees

Classes in weka.classifiers.trees that implement OptionHandler

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 OptionHandler in weka.classifiers.trees.lmt

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

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

Uses of OptionHandler in weka.classifiers.trees.m5

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

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 OptionHandler in weka.clusterers

Classes in weka.clusterers that implement OptionHandler

class	CheckClusterer Class for examining the capabilities and finding problems with clusterers.
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	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 OptionHandler in weka.core

Subinterfaces of OptionHandler in weka.core

interface

DistanceFunction Interface for any class that can compute and return distances between two instances.

Classes in weka.core that implement OptionHandler

class	AllJavadoc Applies all known Javadoc-derived classes to a source file.
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.
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	FindWithCapabilities Locates all classes with certain capabilities.
class	GlobalInfoJavadoc Generates Javadoc comments from the class's globalInfo method.
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	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	OptionHandlerJavadoc Generates Javadoc comments from the OptionHandler's options.
class	TechnicalInformationHandlerJavadoc Generates Javadoc comments from the TechnicalInformationHandler's data.
class	TestInstances Generates artificial datasets for testing.

Methods in weka.core that return OptionHandler

OptionHandler

CheckOptionHandler.getOptionHandler() Get the OptionHandler used in the tests.

Methods in weka.core with parameters of type OptionHandler

void	CheckOptionHandler.setOptionHandler(OptionHandler value) Set the OptionHandler to work on..

Uses of OptionHandler in weka.core.converters

Classes in weka.core.converters that implement OptionHandler

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	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	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	DatabaseLoader Reads Instances from a Database.
class	DatabaseSaver Writes to a database (tested with MySQL, InstantDB, HSQLDB).
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	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	MatlabSaver Writes Matlab ASCII files, in single or double precision format.
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	TextDirectoryLoader Loads all text files in a directory and uses the subdirectory names as class labels.
class	XRFFSaver Writes to a destination that is in the XML version of the ARFF format.

Uses of OptionHandler in weka.core.neighboursearch

Classes in weka.core.neighboursearch that implement OptionHandler

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	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.

Uses of OptionHandler in weka.core.neighboursearch.balltrees

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

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 OptionHandler in weka.core.neighboursearch.kdtrees

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

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 OptionHandler in weka.core.stemmers

Classes in weka.core.stemmers that implement OptionHandler

class

SnowballStemmer A wrapper class for the Snowball stemmers.

Uses of OptionHandler in weka.core.tokenizers

Classes in weka.core.tokenizers that implement OptionHandler

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 OptionHandler in weka.datagenerators

Classes in weka.datagenerators that implement OptionHandler

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.

Uses of OptionHandler in weka.datagenerators.classifiers.classification

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

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 OptionHandler in weka.datagenerators.classifiers.regression

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

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 OptionHandler in weka.datagenerators.clusterers

Classes in weka.datagenerators.clusterers that implement OptionHandler

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 OptionHandler in weka.estimators

Classes in weka.estimators that implement OptionHandler

class	CheckEstimator Class for examining the capabilities and finding problems with estimators.
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 OptionHandler in weka.experiment

Classes in weka.experiment that implement OptionHandler

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	DatabaseResultProducer Examines a database and extracts out the results produced by the specified ResultProducer and submits them to the specified ResultListener.
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	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	PairedTTester Calculates T-Test statistics on data stored in a set of instances.
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	RemoteExperiment Holds all the necessary configuration information for a distributed experiment.
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.

Uses of OptionHandler in weka.filters

Classes in weka.filters that implement OptionHandler

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 OptionHandler in weka.filters.supervised.attribute

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

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 OptionHandler in weka.filters.supervised.instance

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

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 OptionHandler in weka.filters.unsupervised.attribute

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

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	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 OptionHandler in weka.filters.unsupervised.instance

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

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	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 OptionHandler in weka.gui

Classes in weka.gui that implement OptionHandler

class

Main Menu-based GUI for Weka, replacement for the GUIChooser.

Uses of OptionHandler in weka.gui.explorer

Classes in weka.gui.explorer that implement OptionHandler

class	AbstractPlotInstances Abstract superclass for generating plottable instances.
class	ClassifierErrorsPlotInstances A class for generating plottable visualization errors.
class	ClustererAssignmentsPlotInstances A class for generating plottable cluster assignments.

Uses of OptionHandler in weka.gui.scripting

Classes in weka.gui.scripting that implement OptionHandler

class	GroovyScript Represents a Groovy script.
class	JythonScript Represents a Jython script.
class	Script A simple helper class for loading, saving scripts.