Uses of Interface
weka.filters.SupervisedFilter

Packages that use SupervisedFilter

weka.filters.supervised.attribute
weka.filters.supervised.instance

Uses of SupervisedFilter in weka.filters.supervised.attribute

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

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	NominalToBinary Converts all nominal attributes into binary numeric attributes.
class	PLSFilter Runs Partial Least Square Regression over the given instances and computes the resulting beta matrix for prediction. By default it replaces missing values and centers the data. For more information see: Tormod Naes, Tomas Isaksson, Tom Fearn, Tony Davies (2002).

Uses of SupervisedFilter in weka.filters.supervised.instance

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

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	SMOTE Resamples a dataset by applying the Synthetic Minority Oversampling TEchnique (SMOTE).
class	SpreadSubsample Produces a random subsample of a dataset.
class	StratifiedRemoveFolds This filter takes a dataset and outputs a specified fold for cross validation.