Uses of Interface
weka.core.WeightedInstancesHandler

Packages that use WeightedInstancesHandler

Package	Description
weka.classifiers.bayes
weka.classifiers.bayes.net
weka.classifiers.functions
weka.classifiers.lazy
weka.classifiers.meta
weka.classifiers.misc
weka.classifiers.rules
weka.classifiers.trees
weka.classifiers.trees.lmt
weka.clusterers
weka.core.converters
weka.filters
weka.filters.supervised.attribute
weka.filters.supervised.instance
weka.filters.unsupervised.attribute
weka.filters.unsupervised.instance

Uses of WeightedInstancesHandler in weka.classifiers.bayes

Classes in weka.classifiers.bayes that implement WeightedInstancesHandler

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 WeightedInstancesHandler in weka.classifiers.bayes.net

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

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 WeightedInstancesHandler in weka.classifiers.functions

Classes in weka.classifiers.functions that implement WeightedInstancesHandler

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	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.

Uses of WeightedInstancesHandler in weka.classifiers.lazy

Classes in weka.classifiers.lazy that implement WeightedInstancesHandler

Modifier and Type	Class	Description
class	IBk	K-nearest neighbours classifier.
class	LWL	Locally weighted learning.

Uses of WeightedInstancesHandler in weka.classifiers.meta

Classes in weka.classifiers.meta that implement WeightedInstancesHandler

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	FilteredClassifier	Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
class	LogitBoost	Class for performing additive logistic regression.
class	MultiClassClassifier	A metaclassifier for handling multi-class datasets with 2-class classifiers.
class	MultiClassClassifierUpdateable	A metaclassifier for handling multi-class datasets with 2-class classifiers.
class	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	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 WeightedInstancesHandler in weka.classifiers.misc

Classes in weka.classifiers.misc that implement WeightedInstancesHandler

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.

Uses of WeightedInstancesHandler in weka.classifiers.rules

Classes in weka.classifiers.rules that implement WeightedInstancesHandler

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	JRip.Antd	The single antecedent in the rule, which is composed of an attribute and the corresponding value.
class	JRip.NominalAntd	The antecedent with nominal attribute
class	JRip.NumericAntd	The antecedent with numeric attribute
class	JRip.RipperRule	This class implements a single rule that predicts specified class.
class	PART	Class for generating a PART decision list.
class	Rule	Abstract class of generic rule
class	ZeroR	Class for building and using a 0-R classifier.

Uses of WeightedInstancesHandler in weka.classifiers.trees

Classes in weka.classifiers.trees that implement WeightedInstancesHandler

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	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 WeightedInstancesHandler in weka.classifiers.trees.lmt

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

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 WeightedInstancesHandler in weka.clusterers

Classes in weka.clusterers that implement WeightedInstancesHandler

Modifier and Type	Class	Description
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	MakeDensityBasedClusterer	Class for wrapping a Clusterer to make it return a distribution and density.
class	SimpleKMeans	Cluster data using the k means algorithm.

Uses of WeightedInstancesHandler in weka.core.converters

Classes in weka.core.converters that implement WeightedInstancesHandler

Modifier and Type	Class	Description
class	ArffSaver	Writes to a destination in arff text format.
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	SerializedInstancesSaver	Serializes the instances to a file with extension bsi.
class	XRFFSaver	Writes to a destination that is in the XML version of the ARFF format.

Uses of WeightedInstancesHandler in weka.filters

Classes in weka.filters that implement WeightedInstancesHandler

Modifier and Type	Class	Description
class	AllFilter	A simple instance filter that passes all instances directly through.
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.

Uses of WeightedInstancesHandler in weka.filters.supervised.attribute

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

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 WeightedInstancesHandler in weka.filters.supervised.instance

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

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

Uses of WeightedInstancesHandler in weka.filters.unsupervised.attribute

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

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	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	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	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	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 WeightedInstancesHandler in weka.filters.unsupervised.instance

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

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	RemoveMisclassified	A filter that removes instances which are incorrectly classified.
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	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)