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Class weka.classifiers.CostSensitiveClassifier

java.lang.Object
   |
   +----weka.classifiers.Classifier
           |
           +----weka.classifiers.CostSensitiveClassifier

public class CostSensitiveClassifier
extends Classifier
implements OptionHandler, Drawable
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. If this is not supplied, a cost matrix will be loaded on demand. The name of the on-demand file is the relation name of the training data plus ".cost", and the path to the on-demand file is specified with the -D option.

-D directory
Name of a directory to search for cost files when loading costs on demand (default current directory).

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

Options after -- are passed to the designated classifier.

Author:
Len Trigg (len@intelligenesis.net)

Variable Index

 o MATRIX_ON_DEMAND
 o MATRIX_SUPPLIED
 o TAGS_MATRIX_SOURCE

Constructor Index

 o CostSensitiveClassifier()

Method Index

 o buildClassifier(Instances)
Builds the model of the base learner.
 o classifierTipText()
 o classifyInstance(Instance)
Classifies a given instance by choosing the class with the minimum expected misclassification cost.
 o costMatrixSourceTipText()
 o costMatrixTipText()
 o getClassifier()
Gets the classifier used.
 o getCostMatrix()
Gets the misclassification cost matrix.
 o getCostMatrixSource()
Gets the source location method of the cost matrix.
 o getMinimizeExpectedCost()
Gets the value of MinimizeExpectedCost.
 o getOnDemandDirectory()
Returns the directory that will be searched for cost files when loading on demand.
 o getOptions()
Gets the current settings of the Classifier.
 o getSeed()
Get seed for resampling.
 o globalInfo()
 o graph()
Returns graph describing the classifier (if possible).
 o listOptions()
Returns an enumeration describing the available options
 o main(String[])
Main method for testing this class.
 o minimizeExpectedCostTipText()
 o onDemandDirectoryTipText()
 o seedTipText()
 o setClassifier(Classifier)
Sets the distribution classifier
 o setCostMatrix(CostMatrix)
Sets the misclassification cost matrix.
 o setCostMatrixSource(SelectedTag)
Sets the source location of the cost matrix.
 o setMinimizeExpectedCost(boolean)
Set the value of MinimizeExpectedCost.
 o setOnDemandDirectory(File)
Sets the directory that will be searched for cost files when loading on demand.
 o setOptions(String[])
Parses a given list of options.
 o setSeed(int)
Set seed for resampling.
 o toString()
Output a representation of this classifier

Variables

 o MATRIX_ON_DEMAND
 public static final int MATRIX_ON_DEMAND
 o MATRIX_SUPPLIED
 public static final int MATRIX_SUPPLIED
 o TAGS_MATRIX_SOURCE
 public static final Tag TAGS_MATRIX_SOURCE[]

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. If this is not supplied, a cost matrix will be loaded on demand. The name of the on-demand file is the relation name of the training data plus ".cost", and the path to the on-demand file is specified with the -D option.

-D directory
Name of a directory to search for cost files when loading costs on demand (default current directory).

-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 globalInfo
 public String globalInfo()
Returns:
a description of the classifier suitable for displaying in the explorer/experimenter gui
 o costMatrixSourceTipText
 public String costMatrixSourceTipText()
Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui
 o getCostMatrixSource
 public SelectedTag getCostMatrixSource()
Gets the source location method of the cost matrix. Will be one of MATRIX_ON_DEMAND or MATRIX_SUPPLIED.

Returns:
the cost matrix source.
 o setCostMatrixSource
 public void setCostMatrixSource(SelectedTag newMethod)
Sets the source location of the cost matrix. Values other than MATRIX_ON_DEMAND or MATRIX_SUPPLIED will be ignored.

Parameters:
newMethod - the cost matrix location method.
 o onDemandDirectoryTipText
 public String onDemandDirectoryTipText()
Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui
 o getOnDemandDirectory
 public File getOnDemandDirectory()
Returns the directory that will be searched for cost files when loading on demand.

Returns:
The cost file search directory.
 o setOnDemandDirectory
 public void setOnDemandDirectory(File newDir)
Sets the directory that will be searched for cost files when loading on demand.

Parameters:
newDir - The cost file search directory.
 o minimizeExpectedCostTipText
 public String minimizeExpectedCostTipText()
Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui
 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 classifierTipText
 public String classifierTipText()
Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui
 o setClassifier
 public void setClassifier(Classifier classifier)
Sets the distribution classifier

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

Returns:
the classifier
 o costMatrixTipText
 public String costMatrixTipText()
Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui
 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 seedTipText
 public String seedTipText()
Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui
 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 graph
 public String graph() throws Exception
Returns graph describing the classifier (if possible).

Returns:
the graph of the classifier in dotty format
Throws: Exception
if the classifier cannot be graphed
 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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