weka.classifiers.meta
Class MetaCost

java.lang.Object
  weka.classifiers.Classifier
      weka.classifiers.SingleClassifierEnhancer
          weka.classifiers.RandomizableSingleClassifierEnhancer
              weka.classifiers.meta.MetaCost

All Implemented Interfaces:

java.io.Serializable, java.lang.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:

 @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 console

Options after -- are passed to the designated classifier.

Version:

$Revision: 1.24 $

Author:

Len Trigg ([email protected])

See Also:

Serialized Form

Field Summary

static int	MATRIX_ON_DEMAND load cost matrix on demand
static int	MATRIX_SUPPLIED use explicit matrix
static Tag[]	TAGS_MATRIX_SOURCE Specify possible sources of the cost matrix

Constructor Summary

MetaCost()

Method Summary

java.lang.String	bagSizePercentTipText() Returns the tip text for this property
void	buildClassifier(Instances data) Builds the model of the base learner.
java.lang.String	costMatrixSourceTipText() Returns the tip text for this property
java.lang.String	costMatrixTipText() Returns the tip text for this property
double[]	distributionForInstance(Instance instance) Classifies a given instance after filtering.
int	getBagSizePercent() Gets the size of each bag, as a percentage of the training set size.
Capabilities	getCapabilities() Returns default capabilities of the classifier.
CostMatrix	getCostMatrix() Gets the misclassification cost matrix.
SelectedTag	getCostMatrixSource() Gets the source location method of the cost matrix.
int	getNumIterations() Gets the number of bagging iterations
java.io.File	getOnDemandDirectory() Returns the directory that will be searched for cost files when loading on demand.
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
java.lang.String	getRevision() Returns the revision string.
TechnicalInformation	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.
java.lang.String	globalInfo() Returns a string describing classifier
java.util.Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] argv) Main method for testing this class.
java.lang.String	numIterationsTipText() Returns the tip text for this property
java.lang.String	onDemandDirectoryTipText() Returns the tip text for this property
void	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 iterations
void	setOnDemandDirectory(java.io.File newDir) Sets the directory that will be searched for cost files when loading on demand.
void	setOptions(java.lang.String[] options) Parses a given list of options.
java.lang.String	toString() Output a representation of this classifier

Methods 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

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

MATRIX_ON_DEMAND

public static final int MATRIX_ON_DEMAND

load cost matrix on demand

See Also:

Constant Field Values

MATRIX_SUPPLIED

public static final int MATRIX_SUPPLIED

use explicit matrix

See Also:

Constant Field Values

TAGS_MATRIX_SOURCE

public static final Tag[] TAGS_MATRIX_SOURCE

Specify possible sources of the cost matrix

Constructor Detail

MetaCost

public MetaCost()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

getTechnicalInformation

public TechnicalInformation 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 interface TechnicalInformationHandler

Returns:

the technical information about this class

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class RandomizableSingleClassifierEnhancer

Returns:

an enumeration of all the available options.

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

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 interface OptionHandler

Overrides:

setOptions in class RandomizableSingleClassifierEnhancer

Parameters:

options - the list of options as an array of strings

Throws:

java.lang.Exception - if an option is not supported

getOptions

public java.lang.String[] getOptions()

Gets the current settings of the Classifier.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class RandomizableSingleClassifierEnhancer

Returns:

an array of strings suitable for passing to setOptions

costMatrixSourceTipText

public java.lang.String costMatrixSourceTipText()

Returns the tip text for this property

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 java.lang.String onDemandDirectoryTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getOnDemandDirectory

public java.io.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(java.io.File newDir)

Sets the directory that will be searched for cost files when loading on demand.

Parameters:

newDir - The cost file search directory.

bagSizePercentTipText

public java.lang.String bagSizePercentTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

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.

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.

numIterationsTipText

public java.lang.String numIterationsTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setNumIterations

public void setNumIterations(int numIterations)

Sets the number of bagging iterations

Parameters:

numIterations - the number of iterations to use

getNumIterations

public int getNumIterations()

Gets the number of bagging iterations

Returns:

the maximum number of bagging iterations

costMatrixTipText

public java.lang.String costMatrixTipText()

Returns the tip text for this property

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:

newCostMatrix - the cost matrix

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class SingleClassifierEnhancer

Returns:

the capabilities of this classifier

See Also:

Capabilities

buildClassifier

public void buildClassifier(Instances data)
                     throws java.lang.Exception

Builds the model of the base learner.

Specified by:

buildClassifier in class Classifier

Parameters:

data - the training data

Throws:

java.lang.Exception - if the classifier could not be built successfully

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception

Classifies a given instance after filtering.

Overrides:

distributionForInstance in class Classifier

Parameters:

instance - the instance to be classified

Returns:

the class distribution for the given instance

Throws:

java.lang.Exception - if instance could not be classified successfully

toString

public java.lang.String toString()

Output a representation of this classifier

Overrides:

toString in class java.lang.Object

Returns:

a string representaiton of the classifier

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class Classifier

Returns:

the revision

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

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