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## weka.classifiers.mi
Class MISVM

    java.lang.Object
      weka.classifiers.AbstractClassifier
          weka.classifiers.mi.MISVM

All Implemented Interfaces:
  java.io.Serializable, java.lang.Cloneable, Classifier, CapabilitiesHandler, MultiInstanceCapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler

    public class MISVM

extends AbstractClassifier
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: 5928 $
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.AbstractClassifier
`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 `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:

       -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 `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 `OptionHandler`
  Overrides:
    `getOptions` in class `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 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 `Classifier`
  Specified by:
    `getCapabilities` in interface `CapabilitiesHandler`
  Overrides:
    `getCapabilities` in class `AbstractClassifier`
  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 interface `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

  Specified by:
    `distributionForInstance` in interface `Classifier`
  Overrides:
    `distributionForInstance` in class `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 `RevisionHandler`
  Overrides:
    `getRevision` in class `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)

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