Uses of Interface
weka.core.OptionHandler

Packages that use OptionHandler

weka.associations
weka.attributeSelection
weka.classifiers
weka.classifiers.bayes
weka.classifiers.functions
weka.classifiers.lazy
weka.classifiers.meta
weka.classifiers.misc
weka.classifiers.rules
weka.classifiers.trees
weka.classifiers.trees.lmt
weka.classifiers.trees.m5
weka.clusterers
weka.core
weka.datagenerators
weka.experiment
weka.filters.supervised.attribute
weka.filters.supervised.instance
weka.filters.unsupervised.attribute
weka.filters.unsupervised.instance

Uses of OptionHandler in weka.associations

Classes in weka.associations that implement OptionHandler

class	Apriori Class implementing an Apriori-type algorithm.
class	Tertius Class implementing a Tertius-type algorithm.

Uses of OptionHandler in weka.attributeSelection

Classes in weka.attributeSelection that implement OptionHandler

class	BestFirst Class for performing a best first search.
class	CfsSubsetEval CFS attribute subset evaluator.
class	ChiSquaredAttributeEval Class for Evaluating attributes individually by measuring the chi-squared statistic with respect to the class.
class	ClassifierSubsetEval Classifier subset evaluator.
class	ExhaustiveSearch Class for performing an exhaustive search.
class	ForwardSelection Class for performing a forward selection hill climbing search.
class	GainRatioAttributeEval Class for Evaluating attributes individually by measuring gain ratio with respect to the class.
class	GeneticSearch Class for performing a genetic based search.
class	InfoGainAttributeEval Class for Evaluating attributes individually by measuring information gain with respect to the class.
class	OneRAttributeEval Class for Evaluating attributes individually by using the OneR classifier.
class	PrincipalComponents Class for performing principal components analysis/transformation.
class	RaceSearch Class for performing a racing search.
class	RandomSearch Class for performing a random search.
class	Ranker Class for ranking the attributes evaluated by a AttributeEvaluator Valid options are: -P Specify a starting set of attributes.
class	RankSearch Class for evaluating a attribute ranking (given by a specified evaluator) using a specified subset evaluator.
class	ReliefFAttributeEval Class for Evaluating attributes individually using ReliefF.
class	SVMAttributeEval Class for Evaluating attributes individually by using the SVM classifier.
class	SymmetricalUncertAttributeEval Class for Evaluating attributes individually by measuring symmetrical uncertainty with respect to the class.
class	WrapperSubsetEval Wrapper attribute subset evaluator.

Uses of OptionHandler in weka.classifiers

Classes in weka.classifiers that implement OptionHandler

class	BVDecompose Class for performing a Bias-Variance decomposition on any classifier using the method specified in: R.
class	BVDecomposeSegCVSub This class performs Bias-Variance decomposion on any classifier using the sub-sampled cross-validation procedure as specified in: Geoffrey I.
class	CheckClassifier Class for examining the capabilities and finding problems with classifiers.
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 OptionHandler in weka.classifiers.bayes

Classes in weka.classifiers.bayes that implement OptionHandler

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	BayesNet Base class for a Bayes Network classifier.
class	BayesNetB Class for a Bayes Network classifier based on a hill climbing algorithm for learning structure as described in Buntine, W. (1991).
class	BayesNetB2 Class for a Bayes Network classifier based on Buntines hill climbing algorithm for learning structure, but augmented to allow arc reversal as an operation.
class	BayesNetK2 Class for a Bayes Network classifier based on K2 for learning structure.
class	ComplementNaiveBayes Class for building and using a Complement class Naive Bayes classifier.
class	NaiveBayes Class for a Naive Bayes classifier using estimator classes.
class	NaiveBayesMultinomial The core equation for this classifier: P[Ci|D] = (P[D|Ci] x P[Ci]) / P[D] (Bayes rule) where Ci is class i and D is a document
class	NaiveBayesSimple Class for building and using a simple Naive Bayes classifier.
class	NaiveBayesUpdateable Class for a Naive Bayes classifier using estimator classes.

Uses of OptionHandler in weka.classifiers.functions

Classes in weka.classifiers.functions that implement OptionHandler

class	LeastMedSq Implements a least median sqaured linear regression utilising the existing weka LinearRegression class to form predictions.
class	LinearRegression Class for using linear regression for prediction.
class	Logistic Second implementation for building and using a multinomial logistic regression model with a ridge estimator.
class	MultilayerPerceptron A Classifier that uses backpropagation to classify instances.
class	PaceRegression Class for building pace regression linear models and using them for prediction.
class	RBFNetwork Class that implements a radial basis function network.
class	SimpleLinearRegression Class for learning a simple linear regression model.
class	SimpleLogistic Class for building a logistic regression model using LogitBoost.
class	SMO Implements John C.
class	SMOreg Implements Alex J.Smola and Bernhard Scholkopf sequential minimal optimization algorithm for training a support vector regression using polynomial or RBF kernels.
class	VotedPerceptron Implements the voted perceptron algorithm by Freund and Schapire.
class	Winnow Implements Winnow and Balanced Winnow algorithms by N.

Uses of OptionHandler in weka.classifiers.lazy

Classes in weka.classifiers.lazy that implement OptionHandler

class	IB1 IB1-type 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 implement a lazy learning approach to lessening the attribute-independence assumption of naive Bayes.
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 classifier using Freund & Schapire's Adaboost M1 method.
class	AdditiveRegression Meta classifier that enhances the performance of a regression base classifier.
class	AttributeSelectedClassifier Class for running an arbitrary classifier on data that has been reduced through attribute selection.
class	Bagging Class for bagging a classifier.
class	ClassificationViaRegression Class for doing classification using regression methods.
class	CostSensitiveClassifier This metaclassifier makes its base classifier cost-sensitive.
class	CVParameterSelection Class for performing parameter selection by cross-validation for any classifier.
class	Decorate DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples.
class	END Class for creating a committee of random classifiers.
class	FilteredClassifier Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
class	Grading Implements Grading.
class	HND Class to create levelwise NDs with respect to a given hierarchy of classes.
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 (1999).
class	MultiBoostAB Class for boosting a classifier using the MultiBoosting method.
class	MultiClassClassifier Class for handling multi-class datasets with 2-class distribution 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	ND
class	OrdinalClassClassifier Meta classifier for transforming an ordinal class problem to a series of binary class problems.
class	RacedIncrementalLogitBoost Classifier for incremental learning of large datasets by way of racing logit-boosted committees.
class	RandomCommittee Class for creating a committee of random classifiers.
class	RegressionByDiscretization Class for a regression scheme that employs any distribution classifier on a copy of the data that has the class attribute (equal-width) discretized.
class	Stacking Implements stacking.
class	StackingC Implements StackingC (more efficient version of stacking).
class	ThresholdSelector Class for selecting a threshold on a probability output by a distribution classifier.
class	TreeBasedMultiClassClassifier Class that represents and builds a classifier tree.
class	Vote Class for combining classifiers using unweighted average of probability estimates (classification) or numeric predictions (regression).

Uses of OptionHandler in weka.classifiers.misc

Classes in weka.classifiers.misc that implement OptionHandler

class	FLR Fuzzy Lattice Reasoning Classifier FLR Classifier implementation in WEKA The Fuzzy Lattice Reasoning Classifier uses the notion of Fuzzy Lattices for creating a Reasoning Environment.
class	HyperPipes Class implementing a HyperPipe classifier.
class	VFI Class implementing the voting feature interval classifier.

Uses of OptionHandler in weka.classifiers.rules

Classes in weka.classifiers.rules that implement OptionHandler

class	ConjunctiveRule This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels.
class	DecisionTable Class for building and using a simple decision table majority classifier.
class	JRip This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which is proposed by William W.
class	M5Rules Generates a decision list for regression problems using separate-and-conquer.
class	NNge NNge classifier.
class	OneR Class for building and using a 1R classifier.
class	PART Class for generating a PART decision list.
class	Prism Class for building and using a PRISM rule set for classifcation.
class	Ridor The implementation of a RIpple-DOwn Rule learner.
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	ADTree Class for generating an alternating decision tree.
class	DecisionStump Class for building and using a decision stump.
class	Id3 Class implementing an Id3 decision tree classifier.
class	J48 Class for generating an unpruned or a pruned C4.5 decision tree.
class	LMT Class for "logistic model tree" classifier.
class	M5P M5P.
class	RandomForest Class for constructing random forests.
class	RandomTree Class for constructing a tree that considers K random features at each node.
class	REPTree Fast decision tree learner.
class	UserClassifier Class for generating an user defined decision tree.

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	Cobweb Class implementing the Cobweb and Classit clustering algorithms.
class	EM Simple EM (expectation maximisation) class.
class	FarthestFirst Implements the "Farthest First Traversal Algorithm" by Hochbaum and Shmoys 1985: A best possible heuristic for the k-center problem, Mathematics of Operations Research, 10(2):180-184, as cited by Sanjoy Dasgupta "performance guarantees for hierarchical clustering", colt 2002, sydney works as a fast simple approximate clusterer modelled after SimpleKMeans, might be a useful initializer for it Valid options are: -N Specify the number of clusters to generate.
class	MakeDensityBasedClusterer Class for wrapping a Clusterer to make it return a distribution and density.
class	SimpleKMeans Simple k means clustering class.

Uses of OptionHandler in weka.core

Methods in weka.core with parameters of type OptionHandler

static void

CheckOptionHandler.checkOptionHandler(OptionHandler oh, java.lang.String[] options) Runs some diagnostic tests on an optionhandler object.

Uses of OptionHandler in weka.datagenerators

Classes in weka.datagenerators that implement OptionHandler

class	BIRCHCluster Cluster data generator designed for the BIRCH System Dataset is generated with instances in K clusters.
class	RDG1 Class to generate data randomly by producing a decision list.

Uses of OptionHandler in weka.experiment

Classes in weka.experiment that implement OptionHandler

class	AveragingResultProducer 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 A SplitEvaluator that produces results for a classification scheme on a nominal class attribute, including weighted misclassification costs.
class	CrossValidationResultProducer Generates for each run, carries out an n-fold cross-validation, using the set SplitEvaluator to generate some results.
class	CSVResultListener CSVResultListener outputs the received results in csv format to a Writer
class	DatabaseResultProducer DatabaseResultProducer examines a database and extracts out the results produced by the specified ResultProducer and submits them to the specified ResultListener.
class	Experiment Holds all the necessary configuration information for a standard type experiment.
class	InstanceQuery Convert the results of a database query into instances.
class	InstancesResultListener InstancesResultListener outputs the received results in arff format to a Writer.
class	LearningRateResultProducer LearningRateResultProducer takes the results from a ResultProducer and submits the average to the result listener.
class	PairedCorrectedTTester Behaves the same as PairedTTester, only it uses the corrected resampled t-test statistic.
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.

Uses of OptionHandler in weka.filters.supervised.attribute

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

class	AttributeSelection Filter for doing attribute selection.
class	ClassOrder A filter that sorts the order of classes so that the class values are no longer of in the order of that in the header file after filtered.
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.
class	SpreadSubsample Produces a random subsample of a dataset.
class	StratifiedRemoveFolds This filter takes a dataset and outputs folds suitable 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.
class	AddExpression Applys a mathematical expression involving attributes and numeric constants to a dataset.
class	AddNoise Introduces noise data a random subsample of the dataset by changing a given attribute (attribute must be nominal) Valid options are: -C col Index of the attribute to be changed.
class	ClusterMembership A filter that uses a clusterer to obtain cluster membership probabilites for each input instance and outputs them as new instances.
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. eg: Original attribute values 0.1, 0.2, 0.3, 0.1, 0.3 New attribute values 0.1, 0.1, 0.1, -0.2, -0.2 The range of attributes used is taken in numeric order.
class	MakeIndicator Creates a new dataset with a boolean attribute replacing a nominal attribute.
class	MergeTwoValues Merges two values of a nominal attribute.
class	NumericTransform Transforms numeric attributes using a given transformation method.
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.
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 (It will reduce the number of attributes in the data while preserving much of its variation like PCA, but at a much less computational cost).
class	Remove An instance filter that deletes a range of attributes from the dataset.
class	RemoveType A filter that removes attributes of a given type.
class	RemoveUseless This filter removes attributes that do not vary at all or that vary too much.
class	StringToNominal Converts a string attribute (i.e. unspecified number of values) to nominal (i.e. set number of values).
class	StringToWordVector Converts String attributes into a set of attributes representing word occurrence 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 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	Randomize This filter 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	RemoveMisclassified A filter that removes instances which are incorrectly classified.
class	RemovePercentage This filter removes a given percentage of a dataset.
class	RemoveRange This filter takes a dataset and removes a subset of it.
class	RemoveWithValues Filters instances according to the value of an attribute.