weka.classifiers.mi

Class MIEMDD

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.RandomizableClassifier

weka.classifiers.mi.MIEMDD

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.Randomizable, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler

public class MIEMDD
extends weka.classifiers.RandomizableClassifier
implements weka.core.OptionHandler, weka.core.MultiInstanceCapabilitiesHandler, weka.core.TechnicalInformationHandler

EMDD model builds heavily upon Dietterich's Diverse Density (DD) algorithm.
It is a general framework for MI learning of converting the MI problem to a single-instance setting using EM. In this implementation, we use most-likely cause DD model and only use 3 random selected postive bags as initial starting points of EM.

For more information see:

Qi Zhang, Sally A. Goldman: EM-DD: An Improved Multiple-Instance Learning Technique. In: Advances in Neural Information Processing Systems 14, 1073-108, 2001.

BibTeX:

 @inproceedings{Zhang2001,
    author = {Qi Zhang and Sally A. Goldman},
    booktitle = {Advances in Neural Information Processing Systems 14},
    pages = {1073-108},
    publisher = {MIT Press},
    title = {EM-DD: An Improved Multiple-Instance Learning Technique},
    year = {2001}
 }
 

Valid options are:

 -N <num>
  Whether to 0=normalize/1=standardize/2=neither.
  (default 1=standardize)
 

 -S <num>
  Random number seed.
  (default 1)
 

 -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), Lin Dong (ld21@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static int	FILTER_NONE No normalization/standardization
static int	FILTER_NORMALIZE Normalize training data
static int	FILTER_STANDARDIZE Standardize training data
static weka.core.Tag[]	TAGS_FILTER The filter to apply to the training data

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors

Constructor and Description
MIEMDD()

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(weka.core.Instances train) Builds the classifier
double[]	distributionForInstance(weka.core.Instance exmp) Computes the distribution for a given exemplar
java.lang.String	filterTypeTipText() Returns the tip text for this property
weka.core.Capabilities	getCapabilities() Returns default capabilities of the classifier.
weka.core.SelectedTag	getFilterType() Gets how the training data will be transformed.
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.
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.
void	setFilterType(weka.core.SelectedTag newType) Sets how the training data will be transformed.
void	setOptions(java.lang.String[] options) Parses a given list of options.
java.lang.String	toString() Gets a string describing the classifier.

Methods inherited from class weka.classifiers.RandomizableClassifier

getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, 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

FILTER_NORMALIZE

public static final int FILTER_NORMALIZE

Normalize training data

See Also:

Constant Field Values

FILTER_STANDARDIZE

public static final int FILTER_STANDARDIZE

Standardize training data

See Also:

Constant Field Values

FILTER_NONE

public static final int FILTER_NONE

No normalization/standardization

See Also:

Constant Field Values

TAGS_FILTER

public static final weka.core.Tag[] TAGS_FILTER

The filter to apply to the training data

Constructor Detail

MIEMDD

public MIEMDD()

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.RandomizableClassifier

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:

 -N <num>
  Whether to 0=normalize/1=standardize/2=neither.
  (default 1=standardize)
 

 -S <num>
  Random number seed.
  (default 1)
 

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
 

Specified by:

setOptions in interface weka.core.OptionHandler

Overrides:

setOptions in class weka.classifiers.RandomizableClassifier

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.RandomizableClassifier

Returns:

an array of strings suitable for passing to setOptions

filterTypeTipText

public java.lang.String filterTypeTipText()

Returns the tip text for this property

Returns:

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

getFilterType

public weka.core.SelectedTag getFilterType()

Gets how the training data will be transformed. Will be one of FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.

Returns:

the filtering mode

setFilterType

public void setFilterType(weka.core.SelectedTag newType)

Sets how the training data will be transformed. Should be one of FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.

Parameters:

newType - the new filtering mode

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.AbstractClassifier

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 train)
                     throws java.lang.Exception

Builds the classifier

Specified by:

buildClassifier in interface weka.classifiers.Classifier

Parameters:

train - 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)