Uses of Interface
weka.core.WeightedInstancesHandler

Packages that use WeightedInstancesHandler

weka.classifiers.bayes
weka.classifiers.functions
weka.classifiers.lazy
weka.classifiers.meta
weka.classifiers.misc
weka.classifiers.rules
weka.classifiers.trees
weka.classifiers.trees.lmt
weka.clusterers
weka.filters.supervised.attribute
weka.filters.unsupervised.attribute

Uses of WeightedInstancesHandler in weka.classifiers.bayes

Classes in weka.classifiers.bayes that implement WeightedInstancesHandler

class	AODE AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less detrimental) independence assumptions than naive Bayes.
class	BayesNet Base class for a Bayes Network classifier.
class	BayesNetB Class for a Bayes Network classifier based on a hill climbing algorithm for learning structure as described in Buntine, W. (1991).
class	BayesNetB2 Class for a Bayes Network classifier based on Buntines hill climbing algorithm for learning structure, but augmented to allow arc reversal as an operation.
class	BayesNetK2 Class for a Bayes Network classifier based on K2 for learning structure.
class	ComplementNaiveBayes Class for building and using a Complement class Naive Bayes classifier.
class	NaiveBayes Class for a Naive Bayes classifier using estimator classes.
class	NaiveBayesMultinomial The core equation for this classifier: P[Ci|D] = (P[D|Ci] x P[Ci]) / P[D] (Bayes rule) where Ci is class i and D is a document
class	NaiveBayesUpdateable Class for a Naive Bayes classifier using estimator classes.

Uses of WeightedInstancesHandler in weka.classifiers.functions

Classes in weka.classifiers.functions that implement WeightedInstancesHandler

class	LinearRegression Class for using linear regression for prediction.
class	Logistic Second implementation for building and using a multinomial logistic regression model with a ridge estimator.
class	MultilayerPerceptron A Classifier that uses backpropagation to classify instances.
class	PaceRegression Class for building pace regression linear models and using them for prediction.
class	SimpleLinearRegression Class for learning a simple linear regression model.
class	SimpleLogistic Class for building a logistic regression model using LogitBoost.
class	SMO Implements John C.
class	SMOreg Implements Alex J.Smola and Bernhard Scholkopf sequential minimal optimization algorithm for training a support vector regression using polynomial or RBF kernels.

Uses of WeightedInstancesHandler in weka.classifiers.lazy

Classes in weka.classifiers.lazy that implement WeightedInstancesHandler

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

class	AdaBoostM1 Class for boosting a classifier using Freund & Schapire's Adaboost M1 method.
class	AdditiveRegression Meta classifier that enhances the performance of a regression base classifier.
class	Bagging Class for bagging a classifier.
class	END Class for creating a committee of random classifiers.
class	LogitBoost Class for performing additive logistic regression..
class	MultiBoostAB Class for boosting a classifier using the MultiBoosting method.
class	RandomCommittee Class for creating a committee of random classifiers.

Uses of WeightedInstancesHandler in weka.classifiers.misc

Classes in weka.classifiers.misc that implement WeightedInstancesHandler

class

VFI Class implementing the voting feature interval classifier.

Uses of WeightedInstancesHandler in weka.classifiers.rules

Classes in weka.classifiers.rules that implement WeightedInstancesHandler

class	ConjunctiveRule This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels.
class	DecisionTable Class for building and using a simple decision table majority classifier.
class	JRip This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which is proposed by William W.
private class	JRip.Antd The single antecedent in the rule, which is composed of an attribute and the corresponding value.
private class	JRip.NominalAntd The antecedent with nominal attribute
private class	JRip.NumericAntd The antecedent with numeric attribute
protected class	JRip.RipperRule This class implements a single rule that predicts specified class.
class	PART Class for generating a PART decision list.
class	Ridor The implementation of a RIpple-DOwn Rule learner.
private class	Ridor.RidorRule This class implements a single rule that predicts the 2-class distribution.
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

class	ADTree Class for generating an alternating decision tree.
class	DecisionStump Class for building and using a decision stump.
class	J48 Class for generating an unpruned or a pruned C4.5 decision tree.
class	RandomForest Class for constructing random forests.
class	RandomTree Class for constructing a tree that considers K random features at each node.
class	REPTree Fast decision tree learner.

Uses of WeightedInstancesHandler in weka.classifiers.trees.lmt

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

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

class	EM Simple EM (expectation maximisation) class.
class	MakeDensityBasedClusterer Class for wrapping a Clusterer to make it return a distribution and density.
class	SimpleKMeans Simple k means clustering class.

Uses of WeightedInstancesHandler in weka.filters.supervised.attribute

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

class

Discretize An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.

Uses of WeightedInstancesHandler in weka.filters.unsupervised.attribute

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

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.