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java.lang.Object | +----weka.attributeSelection.ASEvaluation | +----weka.attributeSelection.SubsetEvaluator | +----weka.attributeSelection.WrapperSubsetEval
For more information see:
Kohavi, R., John G., Wrappers for Feature Subset Selection.
In Artificial Intelligence journal, special issue on relevance,
Vol. 97, Nos 1-2, pp.273-324.
Valid options are:
-B
Class name of base learner to use for accuracy estimation.
Place any classifier options last on the command line following a
"--". Eg -B weka.classifiers.NaiveBayes ... -- -K
-F
-T
-B
-F
-T
Number of cross validation folds to use for estimating accuracy.
Threshold by which to execute another cross validation (standard deviation
---expressed as a percentage of the mean).
WrapperSubsetEval()
buildEvaluator(Instances)
classifierTipText()
evaluateSubset(BitSet)
foldsTipText()
getClassifier()
getFolds()
getOptions()
getSeed()
getThreshold()
globalInfo()
listOptions()
main(String[])
seedTipText()
setClassifier(Classifier)
setFolds(int)
setOptions(String[])
setSeed(int)
setThreshold(double)
thresholdTipText()
toString()
WrapperSubsetEval
public WrapperSubsetEval()
Constructor. Calls restOptions to set default options
globalInfo
public java.lang.String globalInfo()
Returns a string describing this attribute evaluator
listOptions
public java.util.Enumeration listOptions()
Returns an enumeration describing the available options
setOptions
public void setOptions(java.lang.String options[]) throws java.lang.Exception
Parses a given list of options.
Valid options are:
Class name of base learner to use for accuracy estimation.
Place any classifier options last on the command line following a
"--". Eg -B weka.classifiers.NaiveBayes ... -- -K
Number of cross validation folds to use for estimating accuracy.
Threshold by which to execute another cross validation (standard deviation
---expressed as a percentage of the mean).
options
- the list of options as an array of strings
thresholdTipText
public java.lang.String thresholdTipText()
Returns the tip text for this property
setThreshold
public void setThreshold(double t)
Set the value of the threshold for repeating cross validation
t
- the value of the threshold
getThreshold
public double getThreshold()
Get the value of the threshold
foldsTipText
public java.lang.String foldsTipText()
Returns the tip text for this property
setFolds
public void setFolds(int f)
Set the number of folds to use for accuracy estimation
f
- the number of folds
getFolds
public int getFolds()
Get the number of folds used for accuracy estimation
seedTipText
public java.lang.String seedTipText()
Returns the tip text for this property
setSeed
public void setSeed(int s)
Set the seed to use for cross validation
s
- the seed
getSeed
public int getSeed()
Get the random number seed used for cross validation
classifierTipText
public java.lang.String classifierTipText()
Returns the tip text for this property
setClassifier
public void setClassifier(Classifier newClassifier)
Set the classifier to use for accuracy estimation
newClassifier
- the Classifier to use.
getClassifier
public Classifier getClassifier()
Get the classifier used as the base learner.
getOptions
public java.lang.String[] getOptions()
Gets the current settings of WrapperSubsetEval.
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.
data
- set of instances serving as training data
evaluateSubset
public double evaluateSubset(java.util.BitSet subset) throws java.lang.Exception
Evaluates a subset of attributes
subset
- a bitset representing the attribute subset to be
evaluated
toString
public java.lang.String toString()
Returns a string describing the wrapper
main
public static void main(java.lang.String args[])
Main method for testing this class.
args
- the options
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