jsat.datatransform.featureselection

Class SFS

java.lang.Object

jsat.datatransform.featureselection.SFS

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, DataTransform

public class SFS
extends java.lang.Object
implements DataTransform

Sequential Forward Selection (SFS) is a greedy method of selecting a subset of features to use for prediction. It starts from the set of no features and attempts to add the next best feature to the set at each iteration.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
SFS(int minFeatures, int maxFeatures, ClassificationDataSet dataSet, Classifier evaluater, int folds, double maxIncrease) Performs SFS feature selection for a classification problem
SFS(int minFeatures, int maxFeatures, Classifier evaluater, double maxIncrease) Performs SFS feature selection for a classification problem
SFS(int minFeatures, int maxFeatures, RegressionDataSet dataSet, Regressor regressor, int folds, double maxIncrease) Performs SFS feature selection for a regression problem
SFS(int minFeatures, int maxFeatures, Regressor regressor, double maxIncrease) Creates SFS feature selection for a regression problem

Method Summary

Modifier and Type	Method and Description
SFS	clone()
void	fit(DataSet data) Fits this transform to the given dataset.
int	getFolds()
int	getMaxFeatures() Returns the maximum number of features to find
double	getMaxIncrease()
int	getMinFeatures() Returns the minimum number of features to find
java.util.Set<java.lang.Integer>	getSelectedCategorical() Returns a copy of the set of categorical features selected by the search algorithm
java.util.Set<java.lang.Integer>	getSelectedNumerical() Returns a copy of the set of numerical features selected by the search algorithm.
void	setFolds(int folds) Sets the number of folds to use for cross validation when estimating the error rate
void	setMaxFeatures(int maxFeatures) Sets the maximum number of features that must be selected
void	setMaxIncrease(double maxIncrease) Sets the maximum allowable the maximum tolerable increase in error when a feature is added
void	setMinFeatures(int minFeatures) Sets the minimum number of features that must be selected
DataPoint	transform(DataPoint dp) Returns a new data point that is a transformation of the original data point.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

SFS

public SFS(int minFeatures,
           int maxFeatures,
           Classifier evaluater,
           double maxIncrease)

Performs SFS feature selection for a classification problem

Parameters:

minFeatures - the minimum number of features to find

maxFeatures - the maximum number of features to find

evaluater - the classifier to use in determining accuracy given a feature subset

maxIncrease - the maximum tolerable increase in error when a feature is added

SFS

public SFS(int minFeatures,
           int maxFeatures,
           ClassificationDataSet dataSet,
           Classifier evaluater,
           int folds,
           double maxIncrease)

Performs SFS feature selection for a classification problem

Parameters:

minFeatures - the minimum number of features to find

maxFeatures - the maximum number of features to find

dataSet - the data set to perform feature selection on

evaluater - the classifier to use in determining accuracy given a feature subset

folds - the number of cross validation folds to use in selection

maxIncrease - the maximum tolerable increase in error when a feature is added

SFS

public SFS(int minFeatures,
           int maxFeatures,
           Regressor regressor,
           double maxIncrease)

Creates SFS feature selection for a regression problem

Parameters:

minFeatures - the minimum number of features to find

maxFeatures - the maximum number of features to find

regressor - the regressor to use in determining accuracy given a feature subset

maxIncrease - the maximum tolerable increase in error when a feature is added

SFS

public SFS(int minFeatures,
           int maxFeatures,
           RegressionDataSet dataSet,
           Regressor regressor,
           int folds,
           double maxIncrease)

Performs SFS feature selection for a regression problem

Parameters:

minFeatures - the minimum number of features to find

maxFeatures - the maximum number of features to find

dataSet - the data set to perform feature selection on

regressor - the regressor to use in determining accuracy given a feature subset

folds - the number of cross validation folds to use in selection

maxIncrease - the maximum tolerable increase in error when a feature is added

Method Detail

fit

public void fit(DataSet data)

Description copied from interface: DataTransform

Fits this transform to the given dataset. Some transforms can only be learned from classification or regression datasets. If an incompatible dataset type is given, a FailedToFitException exception may be thrown.

Specified by:

fit in interface DataTransform

Parameters:

data - the dataset to fir this transform to

transform

public DataPoint transform(DataPoint dp)

Description copied from interface: DataTransform

Returns a new data point that is a transformation of the original data point. This new data point is a different object, but may contain the same references as the original data point. It is not guaranteed that you can mutate the transformed point without having a side effect on the original point.

Specified by:

transform in interface DataTransform

Parameters:

dp - the data point to apply a transformation to

Returns:

a transformed data point

clone

public SFS clone()

Specified by:

clone in interface DataTransform

Overrides:

clone in class java.lang.Object

getSelectedCategorical

public java.util.Set<java.lang.Integer> getSelectedCategorical()

Returns a copy of the set of categorical features selected by the search algorithm

Returns:

the set of categorical features to use

getSelectedNumerical

public java.util.Set<java.lang.Integer> getSelectedNumerical()

Returns a copy of the set of numerical features selected by the search algorithm.

Returns:

the set of numeric features to use

setMaxIncrease

public void setMaxIncrease(double maxIncrease)

Sets the maximum allowable the maximum tolerable increase in error when a feature is added

Parameters:

maxIncrease - the maximum allowable the maximum tolerable increase in error when a feature is added

getMaxIncrease

public double getMaxIncrease()

Returns:

the maximum allowable the maximum tolerable increase in error when a feature is added

setMinFeatures

public void setMinFeatures(int minFeatures)

Sets the minimum number of features that must be selected

Parameters:

minFeatures - the minimum number of features to learn

getMinFeatures

public int getMinFeatures()

Returns the minimum number of features to find

Returns:

the minimum number of features to find

setMaxFeatures

public void setMaxFeatures(int maxFeatures)

Sets the maximum number of features that must be selected

Parameters:

maxFeatures - the maximum number of features to find

getMaxFeatures

public int getMaxFeatures()

Returns the maximum number of features to find

Returns:

the maximum number of features to find

setFolds

public void setFolds(int folds)

Sets the number of folds to use for cross validation when estimating the error rate

Parameters:

folds - the number of folds to use for cross validation when estimating the error rate

getFolds

public int getFolds()

Returns:

the number of folds to use for cross validation when estimating the error rate