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: 9144 $

Author:

Lin Dong (ld21@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
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

Constructors

Constructor and Description
MIOptimalBall()

Method Summary

Modifier and Type	Method and Description
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

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)