Uses of Interface
weka.core.CapabilitiesIgnorer

Packages that use CapabilitiesIgnorer

Package	Description
weka.associations
weka.attributeSelection
weka.classifiers
weka.classifiers.bayes
weka.classifiers.bayes.net
weka.classifiers.bayes.net.estimate
weka.classifiers.functions
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.converters
weka.estimators
weka.filters
weka.filters.supervised.attribute
weka.filters.supervised.instance
weka.filters.unsupervised.attribute
weka.filters.unsupervised.instance

Uses of CapabilitiesIgnorer in weka.associations

Classes in weka.associations that implement CapabilitiesIgnorer

Modifier and Type	Class	Description
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	SingleAssociatorEnhancer	Abstract utility class for handling settings common to meta associators that use a single base associator.

Uses of CapabilitiesIgnorer in weka.attributeSelection

Classes in weka.attributeSelection that implement CapabilitiesIgnorer

Modifier and Type	Class	Description
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	ClassifierAttributeEval	ClassifierAttributeEval : Evaluates the worth of an attribute by using a user-specified classifier.
class	ClassifierSubsetEval	Classifier subset evaluator: Evaluates attribute subsets on training data or a separate hold out testing set.
class	CorrelationAttributeEval	CorrelationAttributeEval : Evaluates the worth of an attribute by measuring the correlation (Pearson's) between it and the class. Nominal attributes are considered on a value by value basis by treating each value as an indicator.
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).
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).
class	OneRAttributeEval	OneRAttributeEval : Evaluates the worth of an attribute by using the OneR classifier.
class	PrincipalComponents	Performs a principal components analysis and transformation of the data.
class	ReliefFAttributeEval	ReliefFAttributeEval : Evaluates the worth of an attribute by repeatedly sampling an instance and considering the value of the given attribute for the nearest instance of the same and different class.
class	SymmetricalUncertAttributeEval	SymmetricalUncertAttributeEval : Evaluates the worth of an attribute by measuring the symmetrical uncertainty with respect to the class.
class	UnsupervisedAttributeEvaluator	Abstract unsupervised attribute evaluator.
class	UnsupervisedSubsetEvaluator	Abstract unsupervised attribute subset evaluator.
class	WrapperSubsetEval	WrapperSubsetEval: Evaluates attribute sets by using a learning scheme.

Uses of CapabilitiesIgnorer in weka.classifiers

Classes in weka.classifiers that implement CapabilitiesIgnorer

Modifier and Type	Class	Description
class	AbstractClassifier	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	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 CapabilitiesIgnorer in weka.classifiers.bayes

Classes in weka.classifiers.bayes that implement CapabilitiesIgnorer

Modifier and Type	Class	Description
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 an updateable multinomial Naive Bayes classifier.
class	NaiveBayesUpdateable	Class for a Naive Bayes classifier using estimator classes.

Uses of CapabilitiesIgnorer in weka.classifiers.bayes.net

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

Modifier and Type	Class	Description
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 CapabilitiesIgnorer in weka.classifiers.bayes.net.estimate

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

Modifier and Type	Class	Description
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 CapabilitiesIgnorer in weka.classifiers.functions

Classes in weka.classifiers.functions that implement CapabilitiesIgnorer

Modifier and Type	Class	Description
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 learn a multi-layer perceptron to classify instances.
class	SGD	Implements stochastic gradient descent for learning various linear models (binary class SVM, binary class logistic regression, squared loss, Huber loss and epsilon-insensitive loss 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 CapabilitiesIgnorer in weka.classifiers.lazy

Classes in weka.classifiers.lazy that implement CapabilitiesIgnorer

Modifier and Type	Class	Description
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 CapabilitiesIgnorer in weka.classifiers.meta

Classes in weka.classifiers.meta that implement CapabilitiesIgnorer

Modifier and Type	Class	Description
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	IterativeClassifierOptimizer	Chooses the best number of iterations for an IterativeClassifier such as LogitBoost using cross-validation or a percentage split evaluation.
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	RandomizableFilteredClassifier	Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
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.
class	WeightedInstancesHandlerWrapper	Generic wrapper around any classifier to enable weighted instances support. Uses resampling with weights if the base classifier is not implementing the weka.core.WeightedInstancesHandler interface and there are instance weights other 1.0 present.

Uses of CapabilitiesIgnorer in weka.classifiers.misc

Classes in weka.classifiers.misc that implement CapabilitiesIgnorer

Modifier and Type	Class	Description
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 CapabilitiesIgnorer in weka.classifiers.pmml.consumer

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

Modifier and Type	Class	Description
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 CapabilitiesIgnorer in weka.classifiers.rules

Classes in weka.classifiers.rules that implement CapabilitiesIgnorer

Modifier and Type	Class	Description
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 CapabilitiesIgnorer in weka.classifiers.trees

Classes in weka.classifiers.trees that implement CapabilitiesIgnorer

Modifier and Type	Class	Description
class	DecisionStump	Class for building and using a decision stump.
class	HoeffdingTree	A Hoeffding tree (VFDT) is an incremental, anytime decision tree induction algorithm that is capable of learning from massive data streams, assuming that the distribution generating examples does not change over time.
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 CapabilitiesIgnorer in weka.classifiers.trees.lmt

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

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

Uses of CapabilitiesIgnorer in weka.classifiers.trees.m5

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

Modifier and Type	Class	Description
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 CapabilitiesIgnorer in weka.clusterers

Classes in weka.clusterers that implement CapabilitiesIgnorer

Modifier and Type	Class	Description
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	Canopy	Cluster data using the capopy clustering algorithm, which requires just one pass over the 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	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 CapabilitiesIgnorer in weka.core.converters

Classes in weka.core.converters that implement CapabilitiesIgnorer

Modifier and Type	Class	Description
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 (comma-separated values) format.
class	DatabaseSaver	Writes to a database (tested with MySQL, InstantDB, HSQLDB).
class	DictionarySaver	Writes a dictionary constructed from string attributes in incoming instances to a destination.
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/
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/
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/
class	XRFFSaver	Writes to a destination that is in the XML version of the ARFF format.

Uses of CapabilitiesIgnorer in weka.estimators

Classes in weka.estimators that implement CapabilitiesIgnorer

Modifier and Type	Class	Description
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 CapabilitiesIgnorer in weka.filters

Classes in weka.filters that implement CapabilitiesIgnorer

Modifier and Type	Class	Description
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	RenameRelation	A simple filter that allows the relation name of a set of instances to be altered in various ways.
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 CapabilitiesIgnorer in weka.filters.supervised.attribute

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

Modifier and Type	Class	Description
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	ClassConditionalProbabilities	Converts the values of nominal and/or numeric attributes into class conditional probabilities.
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	MergeNominalValues	Merges values of all nominal attributes among the specified attributes, excluding the class attribute, using the CHAID method, but without considering re-splitting of merged subsets.
class	NominalToBinary	Converts all nominal attributes into binary numeric attributes.
class	PartitionMembership	* A filter that uses a PartitionGenerator to generate partition membership values; filtered instances are composed of these values plus the class attribute (if set in the input data) and rendered as sparse instances.

Uses of CapabilitiesIgnorer in weka.filters.supervised.instance

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

Modifier and Type	Class	Description
class	ClassBalancer	Reweights the instances in the data so that each class has the same total weight.
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 CapabilitiesIgnorer in weka.filters.unsupervised.attribute

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

Modifier and Type	Class	Description
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 attribute's values.
class	AddUserFields	A filter that adds new attributes with user specified type and constant value.
class	AddValues	Adds the labels from the given list to an attribute if they are missing.
class	CartesianProduct	A filter for performing the Cartesian product of a set of nominal attributes.
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	DateToNumeric	A filter for turning date attributes into numeric ones.
class	Discretize	An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
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	FixedDictionaryStringToWordVector	Converts String attributes into a set of attributes representing word occurrence (depending on the tokenizer) information from the text contained in the strings.
class	InterquartileRange	A filter for detecting outliers and extreme values based on interquartile ranges.
class	KernelFilter	Converts the given set of data 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 mathematical expression.
class	MergeInfrequentNominalValues	Merges all values of the specified nominal attributes that are insufficiently frequent.
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	NominalToBinary	Converts all nominal attributes into binary numeric attributes.
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, 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	NumericToDate	A filter for turning numeric attributes into date attributes.
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 attribute values.
class	OrdinalToNumeric	An attribute filter that converts ordinal nominal attributes into numeric ones Valid options are:
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 and forces the number of bins to be equal to the square root of the number of values of the numeric attribute. 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.
class	RandomSubset	Chooses a random subset of non-class 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 attributes. 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
class	RenameNominalValues	Renames the values of nominal attributes.
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	ReplaceMissingWithUserConstant	Replaces all missing values for nominal, string, numeric and date attributes in the dataset with user-supplied constant values.
class	ReplaceWithMissingValue	A filter that can be used to introduce missing values in a dataset.
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 range of string attributes (unspecified number of values) to nominal (set number of values).
class	StringToWordVector	Converts string attributes into a set of numeric 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 values of some previous (or future) instance.
class	Transpose	Transposes the data: instances become attributes and attributes become instances.

Uses of CapabilitiesIgnorer in weka.filters.unsupervised.instance

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

Modifier and Type	Class	Description
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	RemoveDuplicates	Removes all duplicate instances from the first batch of data it receives.
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	Resample	Produces a random subsample of a dataset using either sampling with replacement or without replacement.
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. 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)