weka.attributeSelection

Class CfsSubsetEval

java.lang.Object

weka.attributeSelection.ASEvaluation

weka.attributeSelection.CfsSubsetEval

All Implemented Interfaces:

Serializable, SubsetEvaluator, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler

public class CfsSubsetEval
extends ASEvaluation
implements SubsetEvaluator, OptionHandler, TechnicalInformationHandler

CfsSubsetEval :

Evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them.

Subsets of features that are highly correlated with the class while having low intercorrelation are preferred.

For more information see:

M. A. Hall (1998). Correlation-based Feature Subset Selection for Machine Learning. Hamilton, New Zealand.

BibTeX:

 @phdthesis{Hall1998,
    address = {Hamilton, New Zealand},
    author = {M. A. Hall},
    school = {University of Waikato},
    title = {Correlation-based Feature Subset Selection for Machine Learning},
    year = {1998}
 }
 

Valid options are:

 -M
  Treat missing values as a separate value.

 -L
  Don't include locally predictive attributes.

Version:

$Revision: 8034 $

Author:

Mark Hall (mhall@cs.waikato.ac.nz)

See Also:

Discretize, Serialized Form

Constructor Summary

Constructors

Constructor and Description
CfsSubsetEval() Constructor

Method Summary

Methods

Modifier and Type	Method and Description
void	buildEvaluator(Instances data) Generates a attribute evaluator.
double	evaluateSubset(BitSet subset) evaluates a subset of attributes
Capabilities	getCapabilities() Returns the capabilities of this evaluator.
boolean	getLocallyPredictive() Return true if including locally predictive attributes
boolean	getMissingSeparate() Return true is missing is treated as a separate value
String[]	getOptions() Gets the current settings of CfsSubsetEval
String	getRevision() Returns the revision string.
TechnicalInformation	getTechnicalInformation() Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
String	globalInfo() Returns a string describing this attribute evaluator
Enumeration	listOptions() Returns an enumeration describing the available options.
String	locallyPredictiveTipText() Returns the tip text for this property
static void	main(String[] args) Main method for testing this class.
String	missingSeparateTipText() Returns the tip text for this property
int[]	postProcess(int[] attributeSet) Calls locallyPredictive in order to include locally predictive attributes (if requested).
void	setLocallyPredictive(boolean b) Include locally predictive attributes
void	setMissingSeparate(boolean b) Treat missing as a separate value
void	setOptions(String[] options) Parses and sets a given list of options.
String	toString() returns a string describing CFS

Methods inherited from class weka.attributeSelection.ASEvaluation

forName, makeCopies, runEvaluator

Methods inherited from class java.lang.Object

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

Constructor Detail

CfsSubsetEval

public CfsSubsetEval()

Constructor

Method Detail

globalInfo

public String globalInfo()

Returns a string describing this attribute evaluator

Returns:

a description of the evaluator suitable for displaying in the explorer/experimenter gui

getTechnicalInformation

public TechnicalInformation getTechnicalInformation()

Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

Specified by:

getTechnicalInformation in interface TechnicalInformationHandler

Returns:

the technical information about this class

listOptions

public Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Returns:

an enumeration of all the available options.

setOptions

public void setOptions(String[] options)
                throws Exception

Parses and sets a given list of options.

Valid options are:

 -M
  Treat missing values as a separate value.

 -L
  Don't include locally predictive attributes.

Specified by:

setOptions in interface OptionHandler

Parameters:

options - the list of options as an array of strings

Throws:

Exception - if an option is not supported

locallyPredictiveTipText

public String locallyPredictiveTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setLocallyPredictive

public void setLocallyPredictive(boolean b)

Include locally predictive attributes

Parameters:

b - true or false

getLocallyPredictive

public boolean getLocallyPredictive()

Return true if including locally predictive attributes

Returns:

true if locally predictive attributes are to be used

missingSeparateTipText

public String missingSeparateTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setMissingSeparate

public void setMissingSeparate(boolean b)

Treat missing as a separate value

Parameters:

b - true or false

getMissingSeparate

public boolean getMissingSeparate()

Return true is missing is treated as a separate value

Returns:

true if missing is to be treated as a separate value

getOptions

public String[] getOptions()

Gets the current settings of CfsSubsetEval

Specified by:

getOptions in interface OptionHandler

Returns:

an array of strings suitable for passing to setOptions()

getCapabilities

public Capabilities getCapabilities()

Returns the capabilities of this evaluator.

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class ASEvaluation

Returns:

the capabilities of this evaluator

See Also:

Capabilities

buildEvaluator

public void buildEvaluator(Instances data)
                    throws Exception

Generates a attribute evaluator. Has to initialize all fields of the evaluator that are not being set via options. CFS also discretises attributes (if necessary) and initializes the correlation matrix.

Specified by:

buildEvaluator in class ASEvaluation

Parameters:

data - set of instances serving as training data

Throws:

Exception - if the evaluator has not been generated successfully

evaluateSubset

public double evaluateSubset(BitSet subset)
                      throws Exception

evaluates a subset of attributes

Specified by:

evaluateSubset in interface SubsetEvaluator

Parameters:

subset - a bitset representing the attribute subset to be evaluated

Returns:

the merit

Throws:

Exception - if the subset could not be evaluated

toString

public String toString()

returns a string describing CFS

Overrides:

toString in class Object

Returns:

the description as a string

postProcess

public int[] postProcess(int[] attributeSet)
                  throws Exception

Calls locallyPredictive in order to include locally predictive attributes (if requested).

Overrides:

postProcess in class ASEvaluation

Parameters:

attributeSet - the set of attributes found by the search

Returns:

a possibly ranked list of postprocessed attributes

Throws:

Exception - if postprocessing fails for some reason

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class ASEvaluation

Returns:

the revision

main

public static void main(String[] args)

Main method for testing this class.

Parameters:

args - the options