weka.classifiers.mi
Class MISVM

java.lang.Object
  extended by weka.classifiers.Classifier
      extended by weka.classifiers.mi.MISVM
All Implemented Interfaces:
java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, MultiInstanceCapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler

public class MISVM
extends Classifier
implements OptionHandler, MultiInstanceCapabilitiesHandler, 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:

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
 -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: 1.6 $
Author:
Lin Dong (ld21@cs.waikato.ac.nz)
See Also:
SMO, Serialized Form

Field Summary
static int FILTER_NONE
          No normalization/standardization
static int FILTER_NORMALIZE
          Normalize training data
static int FILTER_STANDARDIZE
          Standardize training data
static Tag[] TAGS_FILTER
          The filter to apply to the training data
 
Constructor Summary
MISVM()
           
 
Method Summary
 void buildClassifier(Instances train)
          Builds the classifier
 java.lang.String cTipText()
          Returns the tip text for this property
 double[] distributionForInstance(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.
 Capabilities getCapabilities()
          Returns default capabilities of the classifier.
 SelectedTag getFilterType()
          Gets how the training data will be transformed.
 Kernel getKernel()
          Gets the kernel to use.
 int getMaxIterations()
          Gets the maximum number of iterations.
 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.
 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 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(SelectedTag newType)
          Sets how the training data will be transformed.
 void setKernel(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.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

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 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 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 Classifier
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:

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
 -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 OptionHandler
Overrides:
setOptions in class Classifier
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 Classifier
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 Kernel getKernel()
Gets the kernel to use.

Returns:
the kernel

setKernel

public void setKernel(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(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 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 Capabilities getCapabilities()
Returns default capabilities of the classifier.

Specified by:
getCapabilities in interface CapabilitiesHandler
Overrides:
getCapabilities in class Classifier
Returns:
the capabilities of this classifier
See Also:
Capabilities

getMultiInstanceCapabilities

public Capabilities getMultiInstanceCapabilities()
Returns the capabilities of this multi-instance classifier for the relational data.

Specified by:
getMultiInstanceCapabilities in interface MultiInstanceCapabilitiesHandler
Returns:
the capabilities of this object
See Also:
Capabilities

buildClassifier

public void buildClassifier(Instances train)
                     throws java.lang.Exception
Builds the classifier

Specified by:
buildClassifier in class 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(Instance exmp)
                                 throws java.lang.Exception
Computes the distribution for a given exemplar

Overrides:
distributionForInstance in class Classifier
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 RevisionHandler
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)