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Class weka.classifiers.CostSensitiveClassifier 

java.lang.Object
   |
   +----weka.classifiers.Classifier
           |
           +----weka.classifiers.CostSensitiveClassifier
public class CostSensitiveClassifier
extends Classifier
implements OptionHandler
This metaclassifier makes its base classifier cost-sensitive. Two methods can be used to introduce cost-sensitivity: reweighting training instances according to the total cost assigned to each class; or predicting the class with minimum expected misclassification cost (rather than the most likely class). The minimum expected cost approach requires that the base classifier be a DistributionClassifier.

Valid options are:

-M
Minimize expected misclassification cost. The base classifier must produce probability estimates i.e. a DistributionClassifier). (default is to reweight training instances according to costs per class)

-W classname
Specify the full class name of a classifier (required).

-C cost file
File name of a cost matrix to use (required).

-S seed
Random number seed used when reweighting by resampling (default 1).

Options after -- are passed to the designated classifier.

Version:
$Revision: 1.1 $
Author:
Len Trigg (len@intelligenesis.net)

Constructor Index

o CostSensitiveClassifier()

Method Index

o buildClassifier(Instances)
Builds the model of the base learner.
o classifyInstance(Instance)
Classifies a given instance by choosing the class with the minimum expected misclassification cost.
o getClassifier()
Gets the distribution classifier used.
o getCostMatrix()
Gets the misclassification cost matrix.
o getMinimizeExpectedCost()
Gets the value of MinimizeExpectedCost.
o getOptions()
Gets the current settings of the Classifier.
o getSeed()
Get seed for resampling.
o listOptions()
Returns an enumeration describing the available options
o main(String[])
Main method for testing this class.
o setClassifier(Classifier)
Sets the distribution classifier
o setCostMatrix(CostMatrix)
Sets the misclassification cost matrix.
o setMinimizeExpectedCost(boolean)
Set the value of MinimizeExpectedCost.
o setOptions(String[])
Parses a given list of options.
o setSeed(int)
Set seed for resampling.
o toString()
Output a representation of this classifier

Constructors

o CostSensitiveClassifier 
 public CostSensitiveClassifier()

Methods

o listOptions 
 public Enumeration listOptions()
Returns an enumeration describing the available options

Returns:
an enumeration of all the available options
o setOptions 
 public void setOptions(String options[]) throws Exception
Parses a given list of options. Valid options are:

-M
Minimize expected misclassification cost. The base classifier must produce probability estimates i.e. a DistributionClassifier). (default is to reweight training instances according to costs per class)

-W classname
Specify the full class name of a classifier (required).

-C cost file
File name of a cost matrix to use (required).

-S seed
Random number seed used when reweighting by resampling (default 1).

Options after -- are passed to the designated classifier.

Parameters:
options - the list of options as an array of strings
Throws: Exception
if an option is not supported
o getOptions 
 public String[] getOptions()
Gets the current settings of the Classifier.

Returns:
an array of strings suitable for passing to setOptions
o getMinimizeExpectedCost 
 public boolean getMinimizeExpectedCost()
Gets the value of MinimizeExpectedCost.

Returns:
Value of MinimizeExpectedCost.
o setMinimizeExpectedCost 
 public void setMinimizeExpectedCost(boolean newMinimizeExpectedCost)
Set the value of MinimizeExpectedCost.

Parameters:
newMinimizeExpectedCost - Value to assign to MinimizeExpectedCost.
o setClassifier 
 public void setClassifier(Classifier classifier)
Sets the distribution classifier

Parameters:
classifier - the distribution classifier with all options set.
o getClassifier 
 public Classifier getClassifier()
Gets the distribution classifier used.

Returns:
the classifier
o getCostMatrix 
 public CostMatrix getCostMatrix()
Gets the misclassification cost matrix.

Returns:
the cost matrix
o setCostMatrix 
 public void setCostMatrix(CostMatrix newCostMatrix)
Sets the misclassification cost matrix.

Parameters:
the - cost matrix
o setSeed 
 public void setSeed(int seed)
Set seed for resampling.

Parameters:
seed - the seed for resampling
o getSeed 
 public int getSeed()
Get seed for resampling.

Returns:
the seed for resampling
o buildClassifier 
 public void buildClassifier(Instances data) throws Exception
Builds the model of the base learner.

Parameters:
data - the training data
Throws: Exception
if the classifier could not be built successfully
Overrides:
buildClassifier in class Classifier
o classifyInstance 
 public double classifyInstance(Instance instance) throws Exception
Classifies a given instance by choosing the class with the minimum expected misclassification cost.

Parameters:
instance - the instance to be classified
Throws: Exception
if instance could not be classified successfully
Overrides:
classifyInstance in class Classifier
o toString 
 public String toString()
Output a representation of this classifier

Overrides:
toString in class Object
o main 
 public static void main(String argv[])
Main method for testing this class.

Parameters:
argv - should contain the following arguments: -t training file [-T test file] [-c class index] 
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