weka.classifiers.mi

Class MIWrapper

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.SingleClassifierEnhancer

weka.classifiers.mi.MIWrapper

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, weka.classifiers.Classifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.MultiInstanceCapabilitiesHandler, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler

public class MIWrapper
extends weka.classifiers.SingleClassifierEnhancer
implements weka.core.MultiInstanceCapabilitiesHandler, weka.core.OptionHandler, weka.core.TechnicalInformationHandler

A simple Wrapper method for applying standard propositional learners to multi-instance data.

For more information see:

E. T. Frank, X. Xu (2003). Applying propositional learning algorithms to multi-instance data. Department of Computer Science, University of Waikato, Hamilton, NZ.

BibTeX:

 @techreport{Frank2003,
    address = {Department of Computer Science, University of Waikato, Hamilton, NZ},
    author = {E. T. Frank and X. Xu},
    institution = {University of Waikato},
    month = {06},
    title = {Applying propositional learning algorithms to multi-instance data},
    year = {2003}
 }
 

Valid options are:

 -P [1|2|3]
  The method used in testing:
  1.arithmetic average
  2.geometric average
  3.max probability of positive bag.
  (default: 1)
 

 -A [0|1|2|3]
  The type of weight setting for each single-instance:
  0.keep the weight to be the same as the original value;
  1.weight = 1.0
  2.weight = 1.0/Total number of single-instance in the
   corresponding bag
  3. weight = Total number of single-instance / (Total
   number of bags * Total number of single-instance 
   in the corresponding bag).
  (default: 3)
 

 -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
 

Version:

$Revision: 10369 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz), Xin Xu (xx5@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static weka.core.Tag[]	TAGS_TESTMETHOD the test methods
static int	TESTMETHOD_ARITHMETIC arithmetic average
static int	TESTMETHOD_GEOMETRIC geometric average
static int	TESTMETHOD_MAXPROB max probability of positive bag

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors

Constructor and Description
MIWrapper()

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(weka.core.Instances data) Builds the classifier
double[]	distributionForInstance(weka.core.Instance exmp) Computes the distribution for a given exemplar
weka.core.Capabilities	getCapabilities() Returns default capabilities of the classifier.
weka.core.SelectedTag	getMethod() Get the method used in testing.
weka.core.Capabilities	getMultiInstanceCapabilities() Returns the capabilities of this multi-instance classifier for the relational data.
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
java.lang.String	getRevision() Returns the revision string.
weka.core.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.
weka.core.SelectedTag	getWeightMethod() Returns the current weighting method for instances.
java.lang.String	globalInfo() Returns a string describing this filter
java.util.Enumeration<weka.core.Option>	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] argv) Main method for testing this class.
java.lang.String	methodTipText() Returns the tip text for this property
void	setMethod(weka.core.SelectedTag method) Set the method used in testing.
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setWeightMethod(weka.core.SelectedTag method) The new method for weighting the instances.
java.lang.String	toString() Gets a string describing the classifier.
java.lang.String	weightMethodTipText() Returns the tip text for this property

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, getClassifier, postExecution, preExecution, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object

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

Methods inherited from interface weka.core.OptionHandler

makeCopy

Field Detail

TESTMETHOD_ARITHMETIC

public static final int TESTMETHOD_ARITHMETIC

arithmetic average

See Also:

Constant Field Values

TESTMETHOD_GEOMETRIC

public static final int TESTMETHOD_GEOMETRIC

geometric average

See Also:

Constant Field Values

TESTMETHOD_MAXPROB

public static final int TESTMETHOD_MAXPROB

max probability of positive bag

See Also:

Constant Field Values

TAGS_TESTMETHOD

public static final weka.core.Tag[] TAGS_TESTMETHOD

the test methods

Constructor Detail

MIWrapper

public MIWrapper()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing this filter

Returns:

a description of the filter suitable for displaying in the explorer/experimenter gui

getTechnicalInformation

public weka.core.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 weka.core.TechnicalInformationHandler

Returns:

the technical information about this class

listOptions

public java.util.Enumeration<weka.core.Option> listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface weka.core.OptionHandler

Overrides:

listOptions in class weka.classifiers.SingleClassifierEnhancer

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:

 -P [1|2|3]
  The method used in testing:
  1.arithmetic average
  2.geometric average
  3.max probability of positive bag.
  (default: 1)
 

 -A [0|1|2|3]
  The type of weight setting for each single-instance:
  0.keep the weight to be the same as the original value;
  1.weight = 1.0
  2.weight = 1.0/Total number of single-instance in the
   corresponding bag
  3. weight = Total number of single-instance / (Total
   number of bags * Total number of single-instance 
   in the corresponding bag).
  (default: 3)
 

 -W
  Full name of base classifier.
  (default: weka.classifiers.rules.ZeroR)
 

 Options specific to classifier weka.classifiers.rules.ZeroR:
 

Specified by:

setOptions in interface weka.core.OptionHandler

Overrides:

setOptions in class weka.classifiers.SingleClassifierEnhancer

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 weka.core.OptionHandler

Overrides:

getOptions in class weka.classifiers.SingleClassifierEnhancer

Returns:

an array of strings suitable for passing to setOptions

weightMethodTipText

public java.lang.String weightMethodTipText()

Returns the tip text for this property

Returns:

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

setWeightMethod

public void setWeightMethod(weka.core.SelectedTag method)

The new method for weighting the instances.

Parameters:

method - the new method

getWeightMethod

public weka.core.SelectedTag getWeightMethod()

Returns the current weighting method for instances.

Returns:

the current weighting method

methodTipText

public java.lang.String methodTipText()

Returns the tip text for this property

Returns:

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

setMethod

public void setMethod(weka.core.SelectedTag method)

Set the method used in testing.

Parameters:

method - the index of method to use.

getMethod

public weka.core.SelectedTag getMethod()

Get the method used in testing.

Returns:

the index of method used in testing.

getCapabilities

public weka.core.Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface weka.classifiers.Classifier

Specified by:

getCapabilities in interface weka.core.CapabilitiesHandler

Overrides:

getCapabilities in class weka.classifiers.SingleClassifierEnhancer

Returns:

the capabilities of this classifier

getMultiInstanceCapabilities

public weka.core.Capabilities getMultiInstanceCapabilities()

Returns the capabilities of this multi-instance classifier for the relational data.

Specified by:

getMultiInstanceCapabilities in interface weka.core.MultiInstanceCapabilitiesHandler

Returns:

the capabilities of this object

See Also:

Capabilities

buildClassifier

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

Builds the classifier

Specified by:

buildClassifier in interface weka.classifiers.Classifier

Parameters:

data - the training data to be used for generating the boosted classifier.

Throws:

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

distributionForInstance

public double[] distributionForInstance(weka.core.Instance exmp)
                                 throws java.lang.Exception

Computes the distribution for a given exemplar

Specified by:

distributionForInstance in interface weka.classifiers.Classifier

Overrides:

distributionForInstance in class weka.classifiers.AbstractClassifier

Parameters:

exmp - the exemplar for which distribution is computed

Returns:

the distribution

Throws:

java.lang.Exception - if the distribution can't be computed successfully

toString

public java.lang.String toString()

Gets a string describing the classifier.

Overrides:

toString in class java.lang.Object

Returns:

a string describing the classifer built.

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface weka.core.RevisionHandler

Overrides:

getRevision in class weka.classifiers.AbstractClassifier

Returns:

the revision

main

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

Main method for testing this class.

Parameters:

argv - should contain the command line arguments to the scheme (see Evaluation)