Uses of Interface
weka.core.WeightedInstancesHandler

Packages that use WeightedInstancesHandler

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.filters.supervised.attribute
weka.filters.unsupervised.attribute

Uses of WeightedInstancesHandler in weka.classifiers.bayes

Classes in weka.classifiers.bayes that implement WeightedInstancesHandler

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 WeightedInstancesHandler in weka.classifiers.bayes.net

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

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

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	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

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 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	LogitBoost Class for performing additive logistic regression.
class	RandomCommittee Class for building an ensemble of randomizable base classifiers.
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.

Uses of WeightedInstancesHandler in weka.classifiers.misc

Classes in weka.classifiers.misc that implement WeightedInstancesHandler

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

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	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

class	DecisionStump Class for building and using a decision stump.
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

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. 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.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. For more information, see: Ying Yang, Geoffrey I.