Package weka.classifiers.mi
Class MIOptimalBall
java.lang.Object
weka.classifiers.Classifier
weka.classifiers.mi.MIOptimalBall
- All Implemented Interfaces:
Serializable
,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:
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:
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Field Summary
FieldsModifier and TypeFieldDescriptionstatic final int
No normalization/standardizationstatic final int
Normalize training datastatic final int
Standardize training datastatic final Tag[]
The filter to apply to the training data -
Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionvoid
buildClassifier
(Instances data) Builds the classifiervoid
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 instanceReturns the tip text for this propertyvoid
findRadius
(Instances train) Find the maximum radius for the optimal ball.Returns default capabilities of the classifier.Gets how the training data will be transformed.Returns the capabilities of this multi-instance classifier for the relational data.String[]
Gets the current settings of the classifier.Returns the revision string.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.Returns a string describing this filterReturns an enumeration describing the available options.static void
Main method for testing this class.double
minBagDistance
(Instance center, Instance bag) Calculate the distance from one data point to a bagvoid
setFilterType
(SelectedTag newType) Sets how the training data will be transformed.void
setOptions
(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
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Field Details
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FILTER_NORMALIZE
public static final int FILTER_NORMALIZENormalize training data- See Also:
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FILTER_STANDARDIZE
public static final int FILTER_STANDARDIZEStandardize training data- See Also:
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FILTER_NONE
public static final int FILTER_NONENo normalization/standardization- See Also:
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TAGS_FILTER
The filter to apply to the training data
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Constructor Details
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MIOptimalBall
public MIOptimalBall()
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Method Details
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globalInfo
Returns a string describing this filter- Returns:
- a description of the filter suitable for displaying in the explorer/experimenter gui
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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 interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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getCapabilities
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classClassifier
- Returns:
- the capabilities of this classifier
- See Also:
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getMultiInstanceCapabilities
Returns the capabilities of this multi-instance classifier for the relational data.- Specified by:
getMultiInstanceCapabilities
in interfaceMultiInstanceCapabilitiesHandler
- Returns:
- the capabilities of this object
- See Also:
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buildClassifier
Builds the classifier- Specified by:
buildClassifier
in classClassifier
- Parameters:
data
- the training data to be used for generating the boosted classifier.- Throws:
Exception
- if the classifier could not be built successfully
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calculateDistance
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)
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minBagDistance
Calculate the distance from one data point to a bag- Parameters:
center
- the data point in instance spacebag
- the bag- Returns:
- the double value as the distance.
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findRadius
Find the maximum radius for the optimal ball.- Parameters:
train
- the multi-instance data
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sortArray
public double[] sortArray(double[] distance) Sort the array.- Parameters:
distance
- the array need to be sorted- Returns:
- sorted array
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distributionForInstance
Computes the distribution for a given multiple instance- Overrides:
distributionForInstance
in classClassifier
- Parameters:
newBag
- the instance for which distribution is computed- Returns:
- the distribution
- Throws:
Exception
- if the distribution can't be computed successfully
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listOptions
Returns an enumeration describing the available options.- Specified by:
listOptions
in interfaceOptionHandler
- Overrides:
listOptions
in classClassifier
- Returns:
- an enumeration of all the available options.
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getOptions
Gets the current settings of the classifier.- Specified by:
getOptions
in interfaceOptionHandler
- Overrides:
getOptions
in classClassifier
- Returns:
- an array of strings suitable for passing to setOptions
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setOptions
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 interfaceOptionHandler
- Overrides:
setOptions
in classClassifier
- Parameters:
options
- the list of options as an array of strings- Throws:
Exception
- if an option is not supported
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filterTypeTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setFilterType
Sets how the training data will be transformed. Should be one of FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.- Parameters:
newType
- the new filtering mode
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getFilterType
Gets how the training data will be transformed. Will be one of FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.- Returns:
- the filtering mode
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getRevision
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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main
Main method for testing this class.- Parameters:
argv
- should contain the command line arguments to the scheme (see Evaluation)
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