weka.classifiers.mi

Class MISVM

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.mi.MISVM

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 MISVM
extends weka.classifiers.AbstractClassifier
implements weka.core.OptionHandler, weka.core.MultiInstanceCapabilitiesHandler, weka.core.TechnicalInformationHandler

Implements Stuart Andrews' mi_SVM (Maximum pattern Margin Formulation of MIL). Applying weka.classifiers.functions.SMO to solve multiple instances problem.
The algorithm first assign the bag label to each instance in the bag as its initial class label. After that applying SMO to compute SVM solution for all instances in positive bags And then reassign the class label of each instance in the positive bag according to the SVM result Keep on iteration until labels do not change anymore.

For more information see:

Stuart Andrews, Ioannis Tsochantaridis, Thomas Hofmann: Support Vector Machines for Multiple-Instance Learning. In: Advances in Neural Information Processing Systems 15, 561-568, 2003.

BibTeX:

 @inproceedings{Andrews2003,
    author = {Stuart Andrews and Ioannis Tsochantaridis and Thomas Hofmann},
    booktitle = {Advances in Neural Information Processing Systems 15},
    pages = {561-568},
    publisher = {MIT Press},
    title = {Support Vector Machines for Multiple-Instance Learning},
    year = {2003}
 }
 

Valid options are:

 -C <double>
  The complexity constant C. (default 1)
 

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

 -I <num>
  The maximum number of iterations to perform.
  (default: 500)
 

 -K <classname and parameters>
  The Kernel to use.
  (default: weka.classifiers.functions.supportVector.PolyKernel)
 

 Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:
 

 -D
  Enables debugging output (if available) to be printed.
  (default: off)
 

 -no-checks
  Turns off all checks - use with caution!
  (default: checks on)
 

 -C <num>
  The size of the cache (a prime number), 0 for full cache and 
  -1 to turn it off.
  (default: 250007)
 

 -E <num>
  The Exponent to use.
  (default: 1.0)
 

 -L
  Use lower-order terms.
  (default: no)
 

Version:

$Revision: 10369 $

Author:

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

See Also:

SMO, 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
MISVM()

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(weka.core.Instances train) Builds the classifier
java.lang.String	cTipText() Returns the tip text for this property
double[]	distributionForInstance(weka.core.Instance exmp) Computes the distribution for a given exemplar
java.lang.String	filterTypeTipText() Returns the tip text for this property
double	getC() Get the value of C.
weka.core.Capabilities	getCapabilities() Returns default capabilities of the classifier.
weka.core.SelectedTag	getFilterType() Gets how the training data will be transformed.
weka.classifiers.functions.supportVector.Kernel	getKernel() Gets the kernel to use.
int	getMaxIterations() Gets the maximum number of iterations.
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.lang.String	kernelTipText() Returns the tip text for this property
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	maxIterationsTipText() Returns the tip text for this property
void	setC(double v) Set the value of C.
void	setFilterType(weka.core.SelectedTag newType) Sets how the training data will be transformed.
void	setKernel(weka.classifiers.functions.supportVector.Kernel value) Sets the kernel to use.
void	setMaxIterations(int value) Sets the maximum number of iterations.
void	setOptions(java.lang.String[] options) Parses a given list of options.

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, toString, 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

MISVM

public MISVM()

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.AbstractClassifier

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:

 -C <double>
  The complexity constant C. (default 1)
 

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

 -I <num>
  The maximum number of iterations to perform.
  (default: 500)
 

 -K <classname and parameters>
  The Kernel to use.
  (default: weka.classifiers.functions.supportVector.PolyKernel)
 

 Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:
 

 -D
  Enables debugging output (if available) to be printed.
  (default: off)
 

 -no-checks
  Turns off all checks - use with caution!
  (default: checks on)
 

 -C <num>
  The size of the cache (a prime number), 0 for full cache and 
  -1 to turn it off.
  (default: 250007)
 

 -E <num>
  The Exponent to use.
  (default: 1.0)
 

 -L
  Use lower-order terms.
  (default: no)
 

Specified by:

setOptions in interface weka.core.OptionHandler

Overrides:

setOptions in class weka.classifiers.AbstractClassifier

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.AbstractClassifier

Returns:

an array of strings suitable for passing to setOptions

kernelTipText

public java.lang.String kernelTipText()

Returns the tip text for this property

Returns:

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

getKernel

public weka.classifiers.functions.supportVector.Kernel getKernel()

Gets the kernel to use.

Returns:

the kernel

setKernel

public void setKernel(weka.classifiers.functions.supportVector.Kernel value)

Sets the kernel to use.

Parameters:

value - the kernel

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

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

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

cTipText

public java.lang.String cTipText()

Returns the tip text for this property

Returns:

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

getC

public double getC()

Get the value of C.

Returns:

Value of C.

setC

public void setC(double v)

Set the value of C.

Parameters:

v - Value to assign to C.

maxIterationsTipText

public java.lang.String maxIterationsTipText()

Returns the tip text for this property

Returns:

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

getMaxIterations

public int getMaxIterations()

Gets the maximum number of iterations.

Returns:

the maximum number of iterations.

setMaxIterations

public void setMaxIterations(int value)

Sets the maximum number of iterations.

Parameters:

value - the maximum number of iterations.

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

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)