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Class weka.attributeSelection.CfsSubsetEval

java.lang.Object
    |
    +----weka.attributeSelection.ASEvaluation
            |
            +----weka.attributeSelection.SubsetEvaluator
                    |
                    +----weka.attributeSelection.CfsSubsetEval

public class CfsSubsetEval
extends SubsetEvaluator
implements OptionHandler
CFS attribute subset evaluator. For more information see:

Hall, M. A. (1998). Correlation-based Feature Subset Selection for Machine Learning. Thesis submitted in partial fulfilment of the requirements of the degree of Doctor of Philosophy at the University of Waikato.

Valid options are: -M
Treat missing values as a seperate value.

-L
Include locally predictive attributes.

Version:
$Revision: 1.14 $
Author:
Mark Hall (mhall@cs.waikato.ac.nz)

Constructor Index

 o CfsSubsetEval()
Constructor

Method Index

 o buildEvaluator(Instances)
Generates a attribute evaluator.
 o evaluateSubset(BitSet)
evaluates a subset of attributes
 o getLocallyPredictive()
Return true if including locally predictive attributes
 o getMissingSeperate()
Return true is missing is treated as a seperate value
 o getOptions()
Gets the current settings of CfsSubsetEval
 o globalInfo()
Returns a string describing this attribute evaluator
 o listOptions()
Returns an enumeration describing the available options
 o locallyPredictiveTipText()
Returns the tip text for this property
 o main(String[])
Main method for testing this class.
 o missingSeperateTipText()
Returns the tip text for this property
 o postProcess(int[])
Calls locallyPredictive in order to include locally predictive attributes (if requested).
 o setLocallyPredictive(boolean)
Include locally predictive attributes
 o setMissingSeperate(boolean)
Treat missing as a seperate value
 o setOptions(String[])
Parses and sets a given list of options.
 o toString()
returns a string describing CFS

Constructor Detail

 o CfsSubsetEval
public CfsSubsetEval()
          Constructor

Method Detail

 o globalInfo
public java.lang.String globalInfo()
          Returns a string describing this attribute evaluator
Returns:
a description of the evaluator suitable for displaying in the explorer/experimenter gui
 o listOptions
public java.util.Enumeration listOptions()
          Returns an enumeration describing the available options
Returns:
an enumeration of all the available options
 o setOptions
public void setOptions(java.lang.String options[]) throws java.lang.Exception
          Parses and sets a given list of options.

Valid options are: -M
Treat missing values as a seperate value.

-L
Include locally predictive attributes.

Parameters:
options - the list of options as an array of strings
Throws:
java.lang.Exception - if an option is not supported
 o locallyPredictiveTipText
public java.lang.String locallyPredictiveTipText()
          Returns the tip text for this property
Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui
 o setLocallyPredictive
public void setLocallyPredictive(boolean b)
          Include locally predictive attributes
Parameters:
b - true or false
 o getLocallyPredictive
public boolean getLocallyPredictive()
          Return true if including locally predictive attributes
Returns:
true if locally predictive attributes are to be used
 o missingSeperateTipText
public java.lang.String missingSeperateTipText()
          Returns the tip text for this property
Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui
 o setMissingSeperate
public void setMissingSeperate(boolean b)
          Treat missing as a seperate value
Parameters:
b - true or false
 o getMissingSeperate
public boolean getMissingSeperate()
          Return true is missing is treated as a seperate value
Returns:
true if missing is to be treated as a seperate value
 o getOptions
public java.lang.String[] getOptions()
          Gets the current settings of CfsSubsetEval
Returns:
an array of strings suitable for passing to setOptions()
 o buildEvaluator
public void buildEvaluator(Instances data) throws java.lang.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.
Parameters:
data - set of instances serving as training data
Throws:
java.lang.Exception - if the evaluator has not been generated successfully
Overrides:
buildEvaluator in class ASEvaluation
 o evaluateSubset
public double evaluateSubset(java.util.BitSet subset) throws java.lang.Exception
          evaluates a subset of attributes
Parameters:
subset - a bitset representing the attribute subset to be evaluated
Throws:
java.lang.Exception - if the subset could not be evaluated
Overrides:
evaluateSubset in class SubsetEvaluator
 o toString
public java.lang.String toString()
          returns a string describing CFS
Returns:
the description as a string
Overrides:
toString in class java.lang.Object
 o postProcess
public int[] postProcess(int attributeSet[]) throws java.lang.Exception
          Calls locallyPredictive in order to include locally predictive attributes (if requested).
Parameters:
attributeSet - the set of attributes found by the search
Returns:
a possibly ranked list of postprocessed attributes
Throws:
java.lang.Exception - if postprocessing fails for some reason
Overrides:
postProcess in class ASEvaluation
 o main
public static void main(java.lang.String args[])
          Main method for testing this class.
Parameters:
args - the options

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