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.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.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 and 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 and 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	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). Valid options are:
class	HoldOutSubsetEvaluator Abstract attribute subset evaluator capable of evaluating subsets with respect to a data set that is distinct from that used to initialize/ train the subset evaluator.
class	InfoGainAttributeEval InfoGainAttributeEval : Evaluates the worth of an attribute by measuring the information gain with respect to the class. InfoGain(Class,Attribute) = H(Class) - H(Class | Attribute). Valid options are:
class	OneRAttributeEval OneRAttributeEval : Evaluates the worth of an attribute by using the OneR classifier. Valid options are:
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 and 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 and 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 a 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 and 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 and 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 and 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 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, 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.functions.supportVector

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

Modifier and Type	Class and Description
class	CachedKernel Base class for RBFKernel and PolyKernel that implements a simple LRU.
class	Kernel Abstract kernel.
class	NormalizedPolyKernel The normalized polynomial kernel. K(x,y) = <x,y>/sqrt(<x,x><y,y>) where <x,y> = PolyKernel(x,y) Valid options are:
class	PolyKernel The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1)^p Valid options are:
class	PrecomputedKernelMatrixKernel This kernel is based on a static kernel matrix that is read from a file.
class	Puk The Pearson VII function-based universal kernel. For more information see: B.
class	RBFKernel The RBF kernel.
class	StringKernel Implementation of the subsequence kernel (SSK) as described in [1] and of the subsequence kernel with lambda pruning (SSK-LP) as described in [2]. For more information, see Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Christopher J.

Uses of CapabilitiesIgnorer in weka.classifiers.lazy

Classes in weka.classifiers.lazy that implement CapabilitiesIgnorer

Modifier and Type	Class and 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 and 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.
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.

Uses of CapabilitiesIgnorer in weka.classifiers.misc

Classes in weka.classifiers.misc that implement CapabilitiesIgnorer

Modifier and Type	Class and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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	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	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 and 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 and 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	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 and 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	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 to re-split 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 and 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 and 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 attributes 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	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	MergeInfrequentNominalValues Merges all values of the specified nominal attribute that are sufficiently infrequent.
class	MergeManyValues Merges many values of a nominal attribute into one value.
class	MergeTwoValues Merges two values of a nominal attribute into one value.
class	NominalToString Converts a nominal attribute (i.e.
class	Normalize Normalizes all numeric values in the given dataset (apart from the class attribute, if set).
class	NumericCleaner A filter that 'cleanses' the numeric data from values that are too small, too big or very close to a certain value (e.g., 0) and sets these values to a pre-defined default.
class	NumericToBinary Converts all numeric attributes into binary attributes (apart from the class attribute, if set): if the value of the numeric attribute is exactly zero, the value of the new attribute will be zero.
class	NumericToNominal A filter for turning numeric attributes into nominal ones.
class	NumericTransform Transforms numeric attributes using a given transformation method.
class	Obfuscate A simple instance filter that renames the relation, all attribute names and all nominal (and string) attribute values.
class	PartitionedMultiFilter A filter that applies filters on subsets of attributes and assembles the output into a new dataset.
class	PKIDiscretize Discretizes numeric attributes using equal frequency binning, where the number of bins is equal to the square root of the number of non-missing values. For more information, see: Ying Yang, Geoffrey I.
class	PotentialClassIgnorer This filter should be extended by other unsupervised attribute filters to allow processing of the class attribute if that's required.
class	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	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	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 attributes representing word occurrence (depending on the tokenizer) information from the text contained in the strings.
class	SwapValues Swaps two values of a nominal attribute.
class	TimeSeriesDelta An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the difference between the current value and the equivalent attribute attribute value of some previous (or future) instance.
class	TimeSeriesTranslate An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the equivalent attribute values of some previous (or future) instance.
class	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 and 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	ReservoirSample Produces a random subsample of a dataset using the reservoir sampling Algorithm "R" by Vitter.
class	SparseToNonSparse An instance filter that converts all incoming sparse instances into non-sparse format.
class	SubsetByExpression * Filters instances according to a user-specified expression. * * Grammar: * * boolexpr_list ::= boolexpr_list boolexpr_part | boolexpr_part; * * boolexpr_part ::= boolexpr:e {: parser.setResult(e); :} ; * * boolexpr ::= BOOLEAN * | true * | false * | expr < expr * | expr <= expr * | expr > expr * | expr >= expr * | expr = expr * | ( boolexpr ) * | not boolexpr * | boolexpr and boolexpr * | boolexpr or boolexpr * | ATTRIBUTE is STRING * | ATTRIBUTE regexp 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. * - regexp * A regular expression for pattern matching, e.g., '^id.*$' * * 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: * *