Package weka.classifiers.meta
Class MetaCost
java.lang.Object
weka.classifiers.Classifier
weka.classifiers.SingleClassifierEnhancer
weka.classifiers.RandomizableSingleClassifierEnhancer
weka.classifiers.meta.MetaCost
- All Implemented Interfaces:
Serializable
,Cloneable
,CapabilitiesHandler
,OptionHandler
,Randomizable
,RevisionHandler
,TechnicalInformationHandler
public class MetaCost
extends RandomizableSingleClassifierEnhancer
implements TechnicalInformationHandler
This metaclassifier makes its base classifier cost-sensitive using the method specified in
Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive. In: Fifth International Conference on Knowledge Discovery and Data Mining, 155-164, 1999.
This classifier should produce similar results to one created by passing the base learner to Bagging, which is in turn passed to a CostSensitiveClassifier operating on minimum expected cost. The difference is that MetaCost produces a single cost-sensitive classifier of the base learner, giving the benefits of fast classification and interpretable output (if the base learner itself is interpretable). This implementation uses all bagging iterations when reclassifying training data (the MetaCost paper reports a marginal improvement when only those iterations containing each training instance are used in reclassifying that instance). BibTeX:
Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive. In: Fifth International Conference on Knowledge Discovery and Data Mining, 155-164, 1999.
This classifier should produce similar results to one created by passing the base learner to Bagging, which is in turn passed to a CostSensitiveClassifier operating on minimum expected cost. The difference is that MetaCost produces a single cost-sensitive classifier of the base learner, giving the benefits of fast classification and interpretable output (if the base learner itself is interpretable). This implementation uses all bagging iterations when reclassifying training data (the MetaCost paper reports a marginal improvement when only those iterations containing each training instance are used in reclassifying that instance). BibTeX:
@inproceedings{Domingos1999, author = {Pedro Domingos}, booktitle = {Fifth International Conference on Knowledge Discovery and Data Mining}, pages = {155-164}, title = {MetaCost: A general method for making classifiers cost-sensitive}, year = {1999} }Valid options are:
-I <num> Number of bagging iterations. (default 10)
-C <cost file name> 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 -N option.
-N <directory> Name of a directory to search for cost files when loading costs on demand (default current directory).
-cost-matrix <matrix> The cost matrix in Matlab single line format.
-P Size of each bag, as a percentage of the training set size. (default 100)
-S <num> Random number seed. (default 1)
-D If set, classifier is run in debug mode and may output additional info to the console
-W Full name of base classifier. (default: weka.classifiers.rules.ZeroR)
Options specific to classifier weka.classifiers.rules.ZeroR:
-D If set, classifier is run in debug mode and may output additional info to the consoleOptions after -- are passed to the designated classifier.
- Version:
- $Revision: 1.24 $
- Author:
- Len Trigg (len@reeltwo.com)
- See Also:
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Field Summary
FieldsModifier and TypeFieldDescriptionstatic final int
load cost matrix on demandstatic final int
use explicit matrixstatic final Tag[]
Specify possible sources of the cost matrix -
Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionReturns the tip text for this propertyvoid
buildClassifier
(Instances data) Builds the model of the base learner.Returns the tip text for this propertyReturns the tip text for this propertydouble[]
distributionForInstance
(Instance instance) Classifies a given instance after filtering.int
Gets the size of each bag, as a percentage of the training set size.Returns default capabilities of the classifier.Gets the misclassification cost matrix.Gets the source location method of the cost matrix.int
Gets the number of bagging iterationsReturns the directory that will be searched for cost files when loading on demand.String[]
Gets the current settings of the Classifier.Returns the revision string.Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.Returns a string describing classifierReturns an enumeration describing the available options.static void
Main method for testing this class.Returns the tip text for this propertyReturns the tip text for this propertyvoid
setBagSizePercent
(int newBagSizePercent) Sets the size of each bag, as a percentage of the training set size.void
setCostMatrix
(CostMatrix newCostMatrix) Sets the misclassification cost matrix.void
setCostMatrixSource
(SelectedTag newMethod) Sets the source location of the cost matrix.void
setNumIterations
(int numIterations) Sets the number of bagging iterationsvoid
setOnDemandDirectory
(File newDir) Sets the directory that will be searched for cost files when loading on demand.void
setOptions
(String[] options) Parses a given list of options.toString()
Output a representation of this classifierMethods inherited from class weka.classifiers.RandomizableSingleClassifierEnhancer
getSeed, seedTipText, setSeed
Methods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, getClassifier, setClassifier
Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
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Field Details
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MATRIX_ON_DEMAND
public static final int MATRIX_ON_DEMANDload cost matrix on demand- See Also:
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MATRIX_SUPPLIED
public static final int MATRIX_SUPPLIEDuse explicit matrix- See Also:
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TAGS_MATRIX_SOURCE
Specify possible sources of the cost matrix
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Constructor Details
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MetaCost
public MetaCost()
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Method Details
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globalInfo
Returns a string describing classifier- Returns:
- a description suitable for displaying in the explorer/experimenter gui
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getTechnicalInformation
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.- Specified by:
getTechnicalInformation
in interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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listOptions
Returns an enumeration describing the available options.- Specified by:
listOptions
in interfaceOptionHandler
- Overrides:
listOptions
in classRandomizableSingleClassifierEnhancer
- Returns:
- an enumeration of all the available options.
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setOptions
Parses a given list of options. Valid options are:-I <num> Number of bagging iterations. (default 10)
-C <cost file name> 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 -N option.
-N <directory> Name of a directory to search for cost files when loading costs on demand (default current directory).
-cost-matrix <matrix> The cost matrix in Matlab single line format.
-P Size of each bag, as a percentage of the training set size. (default 100)
-S <num> Random number seed. (default 1)
-D If set, classifier is run in debug mode and may output additional info to the console
-W Full name of base classifier. (default: weka.classifiers.rules.ZeroR)
Options specific to classifier weka.classifiers.rules.ZeroR:
-D If set, classifier is run in debug mode and may output additional info to the console
Options after -- are passed to the designated classifier.- Specified by:
setOptions
in interfaceOptionHandler
- Overrides:
setOptions
in classRandomizableSingleClassifierEnhancer
- Parameters:
options
- the list of options as an array of strings- Throws:
Exception
- if an option is not supported
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getOptions
Gets the current settings of the Classifier.- Specified by:
getOptions
in interfaceOptionHandler
- Overrides:
getOptions
in classRandomizableSingleClassifierEnhancer
- Returns:
- an array of strings suitable for passing to setOptions
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costMatrixSourceTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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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.
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setCostMatrixSource
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.
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onDemandDirectoryTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getOnDemandDirectory
Returns the directory that will be searched for cost files when loading on demand.- Returns:
- The cost file search directory.
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setOnDemandDirectory
Sets the directory that will be searched for cost files when loading on demand.- Parameters:
newDir
- The cost file search directory.
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bagSizePercentTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getBagSizePercent
public int getBagSizePercent()Gets the size of each bag, as a percentage of the training set size.- Returns:
- the bag size, as a percentage.
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setBagSizePercent
public void setBagSizePercent(int newBagSizePercent) Sets the size of each bag, as a percentage of the training set size.- Parameters:
newBagSizePercent
- the bag size, as a percentage.
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numIterationsTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setNumIterations
public void setNumIterations(int numIterations) Sets the number of bagging iterations- Parameters:
numIterations
- the number of iterations to use
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getNumIterations
public int getNumIterations()Gets the number of bagging iterations- Returns:
- the maximum number of bagging iterations
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costMatrixTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getCostMatrix
Gets the misclassification cost matrix.- Returns:
- the cost matrix
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setCostMatrix
Sets the misclassification cost matrix.- Parameters:
newCostMatrix
- the cost matrix
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getCapabilities
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classSingleClassifierEnhancer
- Returns:
- the capabilities of this classifier
- See Also:
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buildClassifier
Builds the model of the base learner.- Specified by:
buildClassifier
in classClassifier
- Parameters:
data
- the training data- Throws:
Exception
- if the classifier could not be built successfully
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distributionForInstance
Classifies a given instance after filtering.- Overrides:
distributionForInstance
in classClassifier
- Parameters:
instance
- the instance to be classified- Returns:
- the class distribution for the given instance
- Throws:
Exception
- if instance could not be classified successfully
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toString
Output a representation of this classifier -
getRevision
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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main
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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