All Packages Class Hierarchy This Package Previous Next Index WEKA's home
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)
CfsSubsetEval()
- Constructor
buildEvaluator(Instances)
- Generates a attribute evaluator.
evaluateSubset(BitSet)
- evaluates a subset of attributes
getLocallyPredictive()
- Return true if including locally predictive attributes
getMissingSeperate()
- Return true is missing is treated as a seperate value
getOptions()
- Gets the current settings of CfsSubsetEval
globalInfo()
- Returns a string describing this attribute evaluator
listOptions()
- Returns an enumeration describing the available options
locallyPredictiveTipText()
- Returns the tip text for this property
main(String[])
- Main method for testing this class.
missingSeperateTipText()
- Returns the tip text for this property
postProcess(int[])
- Calls locallyPredictive in order to include locally predictive
attributes (if requested).
setLocallyPredictive(boolean)
- Include locally predictive attributes
setMissingSeperate(boolean)
- Treat missing as a seperate value
setOptions(String[])
- Parses and sets a given list of options.
toString()
- returns a string describing CFS
CfsSubsetEval
public CfsSubsetEval()
Constructor
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
listOptions
public java.util.Enumeration listOptions()
Returns an enumeration describing the available options
- Returns:
- an enumeration of all the available options
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
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
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
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
setMissingSeperate
public void setMissingSeperate(boolean b)
Treat missing as a seperate value
- Parameters:
b
- true or false
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
getOptions
public java.lang.String[] getOptions()
Gets the current settings of CfsSubsetEval
- Returns:
- an array of strings suitable for passing to setOptions()
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
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
toString
public java.lang.String toString()
returns a string describing CFS
- Returns:
- the description as a string
- Overrides:
- toString in class java.lang.Object
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
main
public static void main(java.lang.String args[])
Main method for testing this class.
- Parameters:
args
- the options
All Packages Class Hierarchy This Package Previous Next Index WEKA's home