MIOptimalBall

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## weka.classifiers.mi
Class MIOptimalBall

    java.lang.Object
      weka.classifiers.Classifier
          weka.classifiers.mi.MIOptimalBall

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

    public class MIOptimalBall

extends Classifier
implements OptionHandler, WeightedInstancesHandler, MultiInstanceCapabilitiesHandler, TechnicalInformationHandler

This classifier tries to find a suitable ball in the multiple-instance space, with a certain data point in the instance space as a ball center. The possible ball center is a certain instance in a positive bag. The possible radiuses are those which can achieve the highest classification accuracy. The model selects the maximum radius as the radius of the optimal ball.
For more information about this algorithm, see:
Peter Auer, Ronald Ortner: A Boosting Approach to Multiple Instance Learning. In: 15th European Conference on Machine Learning, 63-74, 2004.

BibTeX:

     @inproceedings{Auer2004,
        author = {Peter Auer and Ronald Ortner},
        booktitle = {15th European Conference on Machine Learning},
        note = {LNAI 3201},
        pages = {63-74},
        publisher = {Springer},
        title = {A Boosting Approach to Multiple Instance Learning},
        year = {2004}
     }

Valid options are:

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

Version:
  $Revision: 1.5 $
Author:
  Lin Dong (ld21@cs.waikato.ac.nz)
See Also:
  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
`MIOptimalBall()`

Method Summary
`void` | `buildClassifier(Instances data)` Builds the classifier
`void` | `calculateDistance(Instances train)` calculate the distances from each instance in a positive bag to each bag.
`double[]` | `distributionForInstance(Instance newBag)` Computes the distribution for a given multiple instance
`java.lang.String` | `filterTypeTipText()` Returns the tip text for this property
`void` | `findRadius(Instances train)` Find the maximum radius for the optimal ball.
`Capabilities` | `getCapabilities()` Returns default capabilities of the classifier.
`SelectedTag` | `getFilterType()` Gets how the training data will be transformed.
`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.
`double` | `minBagDistance(Instance center, Instance bag)` Calculate the distance from one data point to a bag
`void` | `setFilterType(SelectedTag newType)` Sets how the training data will be transformed.
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`double[]` | `sortArray(double[] distance)` Sort the array.

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

### MIOptimalBall

    public MIOptimalBall()

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

### 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 data)
                         throws java.lang.Exception

  Builds the classifier

  Specified by:
    `buildClassifier` in class `Classifier`
  Parameters:
    `data` - the training data to be used for generating the boosted classifier.
  Throws:
    `java.lang.Exception` - if the classifier could not be built successfully

### calculateDistance

    public void calculateDistance(Instances train)

  calculate the distances from each instance in a positive bag to each bag. All result distances are stored in m_Distance[i][j][k], where m_Distance[i][j][k] refers the distances from the jth instance in ith bag to the kth bag

  Parameters:
    `train` - the multi-instance dataset (with relational attribute)

### minBagDistance

    public double minBagDistance(Instance center,
                                 Instance bag)

  Calculate the distance from one data point to a bag

  Parameters:
    `center` - the data point in instance space
    `bag` - the bag
  Returns:
    the double value as the distance.

### findRadius

    public void findRadius(Instances train)

  Find the maximum radius for the optimal ball.

  Parameters:
    `train` - the multi-instance data

### sortArray

    public double[] sortArray(double[] distance)

  Sort the array.

  Parameters:
    `distance` - the array need to be sorted
  Returns:
    sorted array

### distributionForInstance

    public double[] distributionForInstance(Instance newBag)
                                     throws java.lang.Exception

  Computes the distribution for a given multiple instance

  Overrides:
    `distributionForInstance` in class `Classifier`
  Parameters:
    `newBag` - the instance for which distribution is computed
  Returns:
    the distribution
  Throws:
    `java.lang.Exception` - if the distribution can't be computed successfully

### 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.

### 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

### setOptions

    public void setOptions(java.lang.String[] options)
                    throws java.lang.Exception

  Parses a given list of options.

  Valid options are:

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

  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

### 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

### 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)

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