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Class weka.classifiers.CostSensitiveClassifier
java.lang.Object
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+----weka.classifiers.Classifier
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+----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)
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MATRIX_ON_DEMAND
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MATRIX_SUPPLIED
-
-
TAGS_MATRIX_SOURCE
-
-
CostSensitiveClassifier()
-
-
buildClassifier(Instances)
- Builds the model of the base learner.
-
classifierTipText()
-
-
classifyInstance(Instance)
- Classifies a given instance by choosing the class with the minimum
expected misclassification cost.
-
costMatrixSourceTipText()
-
-
costMatrixTipText()
-
-
getClassifier()
- Gets the classifier used.
-
getCostMatrix()
- Gets the misclassification cost matrix.
-
getCostMatrixSource()
- Gets the source location method of the cost matrix.
-
getMinimizeExpectedCost()
- Gets the value of MinimizeExpectedCost.
-
getOnDemandDirectory()
- Returns the directory that will be searched for cost files when
loading on demand.
-
getOptions()
- Gets the current settings of the Classifier.
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getSeed()
- Get seed for resampling.
-
globalInfo()
-
-
graph()
- Returns graph describing the classifier (if possible).
-
listOptions()
- Returns an enumeration describing the available options
-
main(String[])
- Main method for testing this class.
-
minimizeExpectedCostTipText()
-
-
onDemandDirectoryTipText()
-
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seedTipText()
-
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setClassifier(Classifier)
- Sets the distribution classifier
-
setCostMatrix(CostMatrix)
- Sets the misclassification cost matrix.
-
setCostMatrixSource(SelectedTag)
- Sets the source location of the cost matrix.
-
setMinimizeExpectedCost(boolean)
- Set the value of MinimizeExpectedCost.
-
setOnDemandDirectory(File)
- Sets the directory that will be searched for cost files when
loading on demand.
-
setOptions(String[])
- Parses a given list of options.
-
setSeed(int)
- Set seed for resampling.
-
toString()
- Output a representation of this classifier
MATRIX_ON_DEMAND
public static final int MATRIX_ON_DEMAND
MATRIX_SUPPLIED
public static final int MATRIX_SUPPLIED
TAGS_MATRIX_SOURCE
public static final Tag TAGS_MATRIX_SOURCE[]
CostSensitiveClassifier
public CostSensitiveClassifier()
listOptions
public Enumeration listOptions()
- Returns an enumeration describing the available options
- Returns:
- an enumeration of all the available options
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
getOptions
public String[] getOptions()
- Gets the current settings of the Classifier.
- Returns:
- an array of strings suitable for passing to setOptions
globalInfo
public String globalInfo()
- Returns:
- a description of the classifier suitable for
displaying in the explorer/experimenter gui
costMatrixSourceTipText
public String costMatrixSourceTipText()
- Returns:
- tip text for this property suitable for
displaying in the explorer/experimenter gui
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.
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.
onDemandDirectoryTipText
public String onDemandDirectoryTipText()
- Returns:
- tip text for this property suitable for
displaying in the explorer/experimenter gui
getOnDemandDirectory
public File getOnDemandDirectory()
- Returns the directory that will be searched for cost files when
loading on demand.
- Returns:
- The cost file search directory.
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.
minimizeExpectedCostTipText
public String minimizeExpectedCostTipText()
- Returns:
- tip text for this property suitable for
displaying in the explorer/experimenter gui
getMinimizeExpectedCost
public boolean getMinimizeExpectedCost()
- Gets the value of MinimizeExpectedCost.
- Returns:
- Value of MinimizeExpectedCost.
setMinimizeExpectedCost
public void setMinimizeExpectedCost(boolean newMinimizeExpectedCost)
- Set the value of MinimizeExpectedCost.
- Parameters:
- newMinimizeExpectedCost - Value to assign to MinimizeExpectedCost.
classifierTipText
public String classifierTipText()
- Returns:
- tip text for this property suitable for
displaying in the explorer/experimenter gui
setClassifier
public void setClassifier(Classifier classifier)
- Sets the distribution classifier
- Parameters:
- classifier - the classifier with all options set.
getClassifier
public Classifier getClassifier()
- Gets the classifier used.
- Returns:
- the classifier
costMatrixTipText
public String costMatrixTipText()
- Returns:
- tip text for this property suitable for
displaying in the explorer/experimenter gui
getCostMatrix
public CostMatrix getCostMatrix()
- Gets the misclassification cost matrix.
- Returns:
- the cost matrix
setCostMatrix
public void setCostMatrix(CostMatrix newCostMatrix)
- Sets the misclassification cost matrix.
- Parameters:
- the - cost matrix
seedTipText
public String seedTipText()
- Returns:
- tip text for this property suitable for
displaying in the explorer/experimenter gui
setSeed
public void setSeed(int seed)
- Set seed for resampling.
- Parameters:
- seed - the seed for resampling
getSeed
public int getSeed()
- Get seed for resampling.
- Returns:
- the seed for resampling
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
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
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
toString
public String toString()
- Output a representation of this classifier
- Overrides:
- toString in class Object
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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