Class MISVM

All Implemented Interfaces:
Serializable, Cloneable, CapabilitiesHandler, MultiInstanceCapabilitiesHandler, OptionHandler, RevisionHandler, 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: 9144 $
Author:
Lin Dong (ld21@cs.waikato.ac.nz)
See Also:
  • Field Details

    • FILTER_NORMALIZE

      public static final int FILTER_NORMALIZE
      Normalize training data
      See Also:
    • FILTER_STANDARDIZE

      public static final int FILTER_STANDARDIZE
      Standardize training data
      See Also:
    • FILTER_NONE

      public static final int FILTER_NONE
      No normalization/standardization
      See Also:
    • TAGS_FILTER

      public static final Tag[] TAGS_FILTER
      The filter to apply to the training data
  • Constructor Details

    • MISVM

      public MISVM()
  • Method Details

    • globalInfo

      public 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 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(String[] options) throws 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:
      Exception - if an option is not supported
    • getOptions

      public 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 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 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 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 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:
    • 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:
    • buildClassifier

      public void buildClassifier(Instances train) throws Exception
      Builds the classifier
      Specified by:
      buildClassifier in class Classifier
      Parameters:
      train - the training data to be used for generating the boosted classifier.
      Throws:
      Exception - if the classifier could not be built successfully
    • distributionForInstance

      public double[] distributionForInstance(Instance exmp) throws 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:
      Exception - if the distribution can't be computed successfully
    • getRevision

      public 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(String[] argv)
      Main method for testing this class.
      Parameters:
      argv - should contain the command line arguments to the scheme (see Evaluation)