Uses of Interface
weka.core.CapabilitiesHandler

Packages that use CapabilitiesHandler
  • Uses of CapabilitiesHandler in weka.associations

    Classes in weka.associations that implement CapabilitiesHandler
    Modifier and Type
    Class
    Description
    class 
    Abstract scheme for learning associations.
    class 
    Class implementing an Apriori-type algorithm.
    class 
    Class for running an arbitrary associator on data that has been passed through an arbitrary filter.
    class 
    Class implementing the FP-growth algorithm for finding large item sets without candidate generation.
    class 
    Abstract utility class for handling settings common to meta associators that use a single base associator.
  • Uses of CapabilitiesHandler in weka.attributeSelection

    Modifier and Type
    Class
    Description
    class 
    Abstract attribute selection evaluation class
    class 
    Abstract attribute set evaluator.
    class 
    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 :

    Evaluates the worth of an attribute by using a user-specified classifier.
    class 
    Classifier subset evaluator:

    Evaluates attribute subsets on training data or a separate hold out testing set.
    class 
    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 :

    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 
    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 :

    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 :

    Evaluates the worth of an attribute by using the OneR classifier.
    class 
    Performs a principal components analysis and transformation of the data.
    class 
    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 :

    Evaluates the worth of an attribute by measuring the symmetrical uncertainty with respect to the class.
    class 
    Abstract unsupervised attribute evaluator.
    class 
    Abstract unsupervised attribute subset evaluator.
    class 
    WrapperSubsetEval:

    Evaluates attribute sets by using a learning scheme.
  • Uses of CapabilitiesHandler in weka.classifiers

    Classes in weka.classifiers that implement CapabilitiesHandler
    Modifier and Type
    Class
    Description
    class 
    Abstract classifier.
    class 
    Abstract utility class for handling settings common to meta classifiers that build an ensemble from a single base learner.
    class 
    Abstract utility class for handling settings common to meta classifiers that build an ensemble from multiple classifiers.
    class 
    Abstract utility class for handling settings common to meta classifiers that build an ensemble in parallel from a single base learner.
    class 
    Abstract utility class for handling settings common to meta classifiers that build an ensemble in parallel using multiple classifiers.
    class 
    Abstract utility class for handling settings common to randomizable classifiers.
    class 
    Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from a single base learner.
    class 
    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 
    Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble in parallel from a single base learner.
    class 
    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 
    Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from a single base learner.
    class 
    Abstract utility class for handling settings common to meta classifiers that use a single base learner.
  • Uses of CapabilitiesHandler in weka.classifiers.bayes

    Modifier and Type
    Class
    Description
    class 
    Bayes Network learning using various search algorithms and quality measures.
    Base class for a Bayes Network classifier.
    class 
    Class for a Naive Bayes classifier using estimator classes.
    class 
    Class for building and using a multinomial Naive Bayes classifier.
    class 
    Multinomial naive bayes for text data.
    class 
    Class for building and using an updateable multinomial Naive Bayes classifier.
    class 
    Class for a Naive Bayes classifier using estimator classes.
  • Uses of CapabilitiesHandler in weka.classifiers.bayes.net

    Modifier and Type
    Class
    Description
    class 
    Bayes Network learning using various search algorithms and quality measures.
    Base class for a Bayes Network classifier.
    class 
    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 
    Bayes Network learning using various search algorithms and quality measures.
    Base class for a Bayes Network classifier.
  • Uses of CapabilitiesHandler in weka.classifiers.bayes.net.estimate

    Modifier and Type
    Class
    Description
    class 
    Symbolic probability estimator based on symbol counts and a prior.
    class 
    Symbolic probability estimator based on symbol counts and a prior.
  • Uses of CapabilitiesHandler in weka.classifiers.functions

    Modifier and Type
    Class
    Description
    class 
    * Implements Gaussian processes for regression without hyperparameter-tuning.
    class 
    Class for using linear regression for prediction.
    class 
    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 
    A classifier that uses backpropagation to learn a multi-layer perceptron to classify instances.
    class 
    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 
    Implements stochastic gradient descent for learning a linear binary class SVM or binary class logistic regression on text data.
    class 
    Learns a simple linear regression model.
    class 
    Classifier for building linear logistic regression models.
    class 
    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 implements the support vector machine for regression.
    class 
    Implementation of the voted perceptron algorithm by Freund and Schapire.
  • Uses of CapabilitiesHandler in weka.classifiers.functions.supportVector

    Modifier and Type
    Class
    Description
    class 
    Base class for RBFKernel and PolyKernel that implements a simple LRU.
    class 
    Abstract kernel.
    class 
    The normalized polynomial kernel.
    K(x,y) = <x,y>/sqrt(<x,x><y,y>) where <x,y> = PolyKernel(x,y)
    class 
    The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1)^p
    class 
    This kernel is based on a static kernel matrix that is read from a file.
    class 
    The Pearson VII function-based universal kernel.

    For more information see:

    B.
    class 
    The RBF kernel : K(x, y) = exp(-gamma*(x-y)^2)

    Valid options are:
    class 
    Implementation of the subsequence kernel (SSK) as described in [1] and of the subsequence kernel with lambda pruning (SSK-LP) as described in [2].

    For more information, see

    Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Christopher J.
  • Uses of CapabilitiesHandler in weka.classifiers.lazy

    Classes in weka.classifiers.lazy that implement CapabilitiesHandler
    Modifier and Type
    Class
    Description
    class 
    K-nearest neighbours classifier.
    class 
    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 
    Locally weighted learning.
  • Uses of CapabilitiesHandler in weka.classifiers.meta

    Classes in weka.classifiers.meta that implement CapabilitiesHandler
    Modifier and Type
    Class
    Description
    class 
    Class for boosting a nominal class classifier using the Adaboost M1 method.
    class 
    Meta classifier that enhances the performance of a regression base classifier.
    class 
    Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
    class 
    Class for bagging a classifier to reduce variance.
    class 
    Class for doing classification using regression methods.
    class 
    A metaclassifier that makes its base classifier cost sensitive.
    class 
    Class for performing parameter selection by cross-validation for any classifier.

    For more information, see:

    R.
    class 
    Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
    class 
    Chooses the best number of iterations for an IterativeClassifier such as LogitBoost using cross-validation or a percentage split evaluation.
    class 
    Class for performing additive logistic regression.
    class 
    A metaclassifier for handling multi-class datasets with 2-class classifiers.
    class 
    A metaclassifier for handling multi-class datasets with 2-class classifiers.
    class 
    Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
    class 
    Class for building an ensemble of randomizable base classifiers.
    class 
    Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
    class 
    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 
    A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized.
    class 
    Combines several classifiers using the stacking method.
    class 
    Class for combining classifiers.
    class 
    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 CapabilitiesHandler in weka.classifiers.misc

    Classes in weka.classifiers.misc that implement CapabilitiesHandler
    Modifier and Type
    Class
    Description
    class 
    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 
    A wrapper around a serialized classifier model.
  • Uses of CapabilitiesHandler in weka.classifiers.pmml.consumer

    Modifier and Type
    Class
    Description
    class 
    Class implementing import of PMML General Regression model.
    class 
    Class implementing import of PMML Neural Network model.
    class 
    Abstract base class for all PMML classifiers.
    class 
    Class implementing import of PMML Regression model.
    class 
    Class implementing import of PMML RuleSetModel.
    class 
    Implements a PMML SupportVectorMachineModel
    class 
    Class implementing import of PMML TreeModel.
  • Uses of CapabilitiesHandler in weka.classifiers.rules

    Modifier and Type
    Class
    Description
    class 
    Class for building and using a simple decision table majority classifier.

    For more information see:

    Ron Kohavi: The Power of Decision Tables.
    class 
    This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was proposed by William W.
    class 
    Generates a decision list for regression problems using separate-and-conquer.
    class 
    Class for building and using a 1R classifier; in other words, uses the minimum-error attribute for prediction, discretizing numeric attributes.
    class 
    Class for generating a PART decision list.
    class 
    Class for building and using a 0-R classifier.
  • Uses of CapabilitiesHandler in weka.classifiers.trees

    Modifier and Type
    Class
    Description
    class 
    Class for building and using a decision stump.
    class 
    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 
    Class for generating a pruned or unpruned C4.5 decision tree.
    class 
    Classifier for building 'logistic model trees', which are classification trees with logistic regression functions at the leaves.
    class 
    M5Base.
    class 
    Class for constructing a forest of random trees.

    For more information see:

    Leo Breiman (2001).
    class 
    Class for constructing a tree that considers K randomly chosen attributes at each node.
    class 
    Fast decision tree learner.
  • Uses of CapabilitiesHandler in weka.classifiers.trees.j48

    Modifier and Type
    Class
    Description
    class 
    Class for handling a tree structure that can be pruned using C4.5 procedures.
    class 
    Class for handling a tree structure used for classification.
    class 
    Class for handling a naive bayes tree structure used for classification.
    class 
    Class for handling a tree structure that can be pruned using a pruning set.
  • Uses of CapabilitiesHandler in weka.classifiers.trees.lmt

    Modifier and Type
    Class
    Description
    class 
    Class for logistic model tree structure.
    class 
    Base/helper class for building logistic regression models with the LogitBoost algorithm.
  • Uses of CapabilitiesHandler in weka.classifiers.trees.m5

    Modifier and Type
    Class
    Description
    class 
    M5Base.
    class 
    This class encapsulates a linear regression function.
    class 
    Constructs a node for use in an m5 tree or rule
  • Uses of CapabilitiesHandler in weka.clusterers

    Classes in weka.clusterers that implement CapabilitiesHandler
    Modifier and Type
    Class
    Description
    class 
    Abstract clusterer.
    class 
    Abstract clustering model that produces (for each test instance) an estimate of the membership in each cluster (ie.
    class 
    Cluster data using the capopy clustering algorithm, which requires just one pass over the data.
    class 
    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 
    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 
    Cluster data using the FarthestFirst algorithm.

    For more information see:

    Hochbaum, Shmoys (1985).
    class 
    Class for running an arbitrary clusterer on data that has been passed through an arbitrary filter.
    class 
    Hierarchical clustering class.
    class 
    Class for wrapping a Clusterer to make it return a distribution and density.
    class 
    Abstract utility class for handling settings common to randomizable clusterers.
    class 
    Abstract utility class for handling settings common to randomizable clusterers.
    class 
    Abstract utility class for handling settings common to randomizable clusterers.
    class 
    Cluster data using the k means algorithm.
    class 
    Meta-clusterer for enhancing a base clusterer.
  • Uses of CapabilitiesHandler in weka.core

    Subinterfaces of CapabilitiesHandler in weka.core
    Modifier and Type
    Interface
    Description
    interface 
    Multi-Instance classifiers can specify an additional Capabilities object for the data in the relational attribute, since the format of multi-instance data is fixed to "bag/NOMINAL,data/RELATIONAL,class".
    interface 
    This interface can be implemented by algorithms that generate a partition of the instance space (e.g., decision trees).
    Classes in weka.core that implement CapabilitiesHandler
    Modifier and Type
    Class
    Description
    class 
    Locates all classes with certain capabilities.
    Methods in weka.core that return CapabilitiesHandler
    Modifier and Type
    Method
    Description
    FindWithCapabilities.getHandler()
    returns the current set CapabilitiesHandler to generate the dataset for, can be null.
    TestInstances.getHandler()
    returns the current set CapabilitiesHandler to generate the dataset for, can be null
    Capabilities.getOwner()
    returns the owner of this capabilities object
    Methods in weka.core with parameters of type CapabilitiesHandler
    Modifier and Type
    Method
    Description
    void
    FindWithCapabilities.setHandler(CapabilitiesHandler value)
    sets the Capabilities handler to generate the data for.
    void
    TestInstances.setHandler(CapabilitiesHandler value)
    sets the Capabilities handler to generate the data for
    void
    Capabilities.setOwner(CapabilitiesHandler value)
    sets the owner of this capabilities object
    Constructors in weka.core with parameters of type CapabilitiesHandler
    Modifier
    Constructor
    Description
     
    initializes the capabilities for the given owner
  • Uses of CapabilitiesHandler in weka.core.converters

    Classes in weka.core.converters that implement CapabilitiesHandler
    Modifier and Type
    Class
    Description
    class 
    Abstract class for Savers that save to a file Valid options are: -i input arff file
    The input filw in arff format.
    class 
    Abstract class for Saver
    class 
    Writes to a destination in arff text format.
    class 
    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 
    Writes to a destination that is in CSV (comma-separated values) format.
    class 
    Writes to a database (tested with MySQL, InstantDB, HSQLDB).
    class 
    Writes a dictionary constructed from string attributes in incoming instances to a destination.
    class 
    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 
    Writes to a destination that is in libsvm format.

    For more information about libsvm see:

    http://www.csie.ntu.edu.tw/~cjlin/libsvm/
    class 
    Writes Matlab ASCII files, in single or double precision format.
    class 
    Serializes the instances to a file with extension bsi.
    class 
    Writes to a destination that is in svm light format.

    For more information about svm light see:

    http://svmlight.joachims.org/
    class 
    Writes to a destination that is in the XML version of the ARFF format.
  • Uses of CapabilitiesHandler in weka.core.neighboursearch

    Modifier and Type
    Class
    Description
    class 
    Applies the given filter before calling the given neighbour search method.
  • Uses of CapabilitiesHandler in weka.estimators

    Classes in weka.estimators that implement CapabilitiesHandler
    Modifier and Type
    Class
    Description
    class 
    Simple symbolic probability estimator based on symbol counts.
    class 
    Abstract class for all estimators.
    class 
    Simple kernel density estimator.
    class 
    Simple probability estimator that places a single normal distribution over the observed values.
    class 
    Simple probability estimator that places a single normal distribution over the observed values.
    class 
    Simple probability estimator that places a single Poisson distribution over the observed values.
  • Uses of CapabilitiesHandler in weka.filters

    Classes in weka.filters that implement CapabilitiesHandler
    Modifier and Type
    Class
    Description
    class 
    A simple instance filter that passes all instances directly through.
    class 
    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 
    Applies several filters successively.
    class 
    A simple filter that allows the relation name of a set of instances to be altered in various ways.
    class 
    This filter is a superclass for simple batch filters.
    class 
    This filter contains common behavior of the SimpleBatchFilter and the SimpleStreamFilter.
    class 
    This filter is a superclass for simple stream filters.
  • Uses of CapabilitiesHandler in weka.filters.supervised.attribute

    Modifier and Type
    Class
    Description
    class 
    A filter for adding the classification, the class distribution and an error flag to a dataset with a classifier.
    class 
    A supervised attribute filter that can be used to select attributes.
    class 
    Converts the values of nominal and/or numeric attributes into class conditional probabilities.
    class 
    Changes the order of the classes so that the class values are no longer of in the order specified in the header.
    class 
    An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
    class 
    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 
    Converts all nominal attributes into binary numeric attributes.
    class 
    * 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 CapabilitiesHandler in weka.filters.supervised.instance

    Modifier and Type
    Class
    Description
    class 
    Reweights the instances in the data so that each class has the same total weight.
    class 
    Produces a random subsample of a dataset using either sampling with replacement or without replacement.
    The original dataset must fit entirely in memory.
    class 
    Produces a random subsample of a dataset.
    class 
    This filter takes a dataset and outputs a specified fold for cross validation.
  • Uses of CapabilitiesHandler in weka.filters.unsupervised.attribute

    Modifier and Type
    Class
    Description
    class 
    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 
    An instance filter that adds a new attribute to the dataset.
    class 
    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 
    An instance filter that creates a new attribute by applying a mathematical expression to existing attributes.
    class 
    An instance filter that adds an ID attribute to the dataset.
    class 
    An instance filter that changes a percentage of a given attribute's values.
    class 
    A filter that adds new attributes with user specified type and constant value.
    class 
    Adds the labels from the given list to an attribute if they are missing.
    class 
    A filter for performing the Cartesian product of a set of nominal attributes.
    class 
    Centers all numeric attributes in the given dataset to have zero mean (apart from the class attribute, if set).
    class 
    Changes the date format used by a date attribute.
    class 
    Filter that can set and unset the class index.
    class 
    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 
    An instance filter that copies a range of attributes in the dataset.
    class 
    A filter for turning date attributes into numeric ones.
    class 
    An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
    class 
    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 
    Converts String attributes into a set of attributes representing word occurrence (depending on the tokenizer) information from the text contained in the strings.
    class 
    A filter for detecting outliers and extreme values based on interquartile ranges.
    class 
    Converts the given set of data into a kernel matrix.
    class 
    A filter that creates a new dataset with a Boolean attribute replacing a nominal attribute.
    class 
    Modify numeric attributes according to a given mathematical expression.
    class 
    Merges all values of the specified nominal attributes that are insufficiently frequent.
    class 
    Merges many values of a nominal attribute into one value.
    class 
    Merges two values of a nominal attribute into one value.
    class 
    Converts all nominal attributes into binary numeric attributes.
    class 
    Converts a nominal attribute (i.e.
    class 
    Normalizes all numeric values in the given dataset (apart from the class attribute, if set).
    class 
    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 
    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 
    A filter for turning numeric attributes into date attributes.
    class 
    A filter for turning numeric attributes into nominal ones.
    class 
    Transforms numeric attributes using a given transformation method.
    class 
    A simple instance filter that renames the relation, all attribute names and all nominal attribute values.
    class 
    An attribute filter that converts ordinal nominal attributes into numeric ones

    Valid options are:
    class 
    A filter that applies filters on subsets of attributes and assembles the output into a new dataset.
    class 
    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 
    This filter should be extended by other unsupervised attribute filters to allow processing of the class attribute if that's required.
    class 
    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 
    Reduces the dimensionality of the data by projecting it onto a lower dimensional subspace using a random matrix with columns of unit length.
    class 
    Chooses a random subset of non-class attributes, either an absolute number or a percentage.
    class 
    An filter that removes a range of attributes from the dataset.
    class 
    Removes attributes based on a regular expression matched against their names.
    class 
    Removes attributes of a given type.
    class 
    This filter removes attributes that do not vary at all or that vary too much.
    class 
    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 
    Renames the values of nominal attributes.
    class 
    A filter that generates output with a new order of the attributes.
    class 
    Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.
    class 
    Replaces all missing values for nominal, string, numeric and date attributes in the dataset with user-supplied constant values.
    class 
    A filter that can be used to introduce missing values in a dataset.
    class 
    A simple filter for sorting the labels of nominal attributes.
    class 
    Standardizes all numeric attributes in the given dataset to have zero mean and unit variance (apart from the class attribute, if set).
    class 
    Converts a range of string attributes (unspecified number of values) to nominal (set number of values).
    class 
    Converts string attributes into a set of numeric attributes representing word occurrence information from the text contained in the strings.
    class 
    Swaps two values of a nominal attribute.
    class 
    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 
    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 
    Transposes the data: instances become attributes and attributes become instances.
  • Uses of CapabilitiesHandler in weka.filters.unsupervised.instance

    Modifier and Type
    Class
    Description
    class 
    An instance filter that converts all incoming instances into sparse format.
    class 
    Randomly shuffles the order of instances passed through it.
    class 
    Removes all duplicate instances from the first batch of data it receives.
    class 
    This filter takes a dataset and outputs a specified fold for cross validation.
    class 
    Determines which values (frequent or infrequent ones) of an (nominal) attribute are retained and filters the instances accordingly.
    class 
    A filter that removes instances which are incorrectly classified.
    class 
    A filter that removes a given percentage of a dataset.
    class 
    A filter that removes a given range of instances of a dataset.
    class 
    Filters instances according to the value of an attribute.
    class 
    Produces a random subsample of a dataset using either sampling with replacement or without replacement.
    class 
    Produces a random subsample of a dataset using the reservoir sampling Algorithm "R" by Vitter.
    class 
    An instance filter that converts all incoming sparse instances into non-sparse format.
    class 
    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)