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## weka.classifiers.mi
Class MIBoost

    java.lang.Object
      weka.classifiers.Classifier
          weka.classifiers.SingleClassifierEnhancer
              weka.classifiers.mi.MIBoost

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

    public class MIBoost

extends SingleClassifierEnhancer
implements OptionHandler, MultiInstanceCapabilitiesHandler, TechnicalInformationHandler

MI AdaBoost method, considers the geometric mean of posterior of instances inside a bag (arithmatic mean of log-posterior) and the expectation for a bag is taken inside the loss function.
For more information about Adaboost, see:
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.

BibTeX:

     @inproceedings{Freund1996,
        address = {San Francisco},
        author = {Yoav Freund and Robert E. Schapire},
        booktitle = {Thirteenth International Conference on Machine Learning},
        pages = {148-156},
        publisher = {Morgan Kaufmann},
        title = {Experiments with a new boosting algorithm},
        year = {1996}
     }

Valid options are:

     -D
      Turn on debugging output.

     -B <num>
      The number of bins in discretization
      (default 0, no discretization)

     -R <num>
      Maximum number of boost iterations.
      (default 10)

     -W <class name>
      Full name of classifier to boost.
      eg: weka.classifiers.bayes.NaiveBayes

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

Version:
  $Revision: 1.6 $
Author:
  Eibe Frank (eibe@cs.waikato.ac.nz), Xin Xu (xx5@cs.waikato.ac.nz)
See Also:
  Serialized Form

Constructor Summary
`MIBoost()`

Method Summary
`void` | `buildClassifier(Instances exps)` Builds the classifier
`java.lang.String` | `discretizeBinTipText()` Returns the tip text for this property
`double[]` | `distributionForInstance(Instance exmp)` Computes the distribution for a given exemplar
`Capabilities` | `getCapabilities()` Returns default capabilities of the classifier.
`int` | `getDiscretizeBin()` Get the number of bins in discretization
`int` | `getMaxIterations()` Get the maximum number of boost 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.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` | `setDiscretizeBin(int bin)` Set the number of bins in discretization
`void` | `setMaxIterations(int maxIterations)` Set the maximum number of boost iterations
`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.SingleClassifierEnhancer
`classifierTipText, getClassifier, setClassifier`

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, wait, wait, wait`

Constructor Detail

### MIBoost

    public MIBoost()

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 `SingleClassifierEnhancer`
  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
        Turn on debugging output.

       -B <num>
        The number of bins in discretization
        (default 0, no discretization)

       -R <num>
        Maximum number of boost iterations.
        (default 10)

       -W <class name>
        Full name of classifier to boost.
        eg: weka.classifiers.bayes.NaiveBayes

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

  Specified by:
    `setOptions` in interface `OptionHandler`
  Overrides:
    `setOptions` in class `SingleClassifierEnhancer`
  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 `SingleClassifierEnhancer`
  Returns:
    an array of strings suitable for passing to setOptions

### 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

### setMaxIterations

    public void setMaxIterations(int maxIterations)

  Set the maximum number of boost iterations

  Parameters:
    `maxIterations` - the maximum number of boost iterations

### getMaxIterations

    public int getMaxIterations()

  Get the maximum number of boost iterations

  Returns:
    the maximum number of boost iterations

### discretizeBinTipText

    public java.lang.String discretizeBinTipText()

  Returns the tip text for this property

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

### setDiscretizeBin

    public void setDiscretizeBin(int bin)

  Set the number of bins in discretization

  Parameters:
    `bin` - the number of bins in discretization

### getDiscretizeBin

    public int getDiscretizeBin()

  Get the number of bins in discretization

  Returns:
    the number of bins in discretization

### getCapabilities

    public Capabilities getCapabilities()

  Returns default capabilities of the classifier.

  Specified by:
    `getCapabilities` in interface `CapabilitiesHandler`
  Overrides:
    `getCapabilities` in class `SingleClassifierEnhancer`
  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 exps)
                         throws java.lang.Exception

  Builds the classifier

  Specified by:
    `buildClassifier` in class `Classifier`
  Parameters:
    `exps` - 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 classification
  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 `RevisionHandler`
  Overrides:
    `getRevision` in class `Classifier`
  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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