weka.classifiers.mi
Class MIOptimalBall

java.lang.Object
  extended by weka.classifiers.Classifier
      extended by 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)