Package weka.classifiers.mi
Class MIOptimalBall
- java.lang.Object
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- weka.classifiers.Classifier
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- weka.classifiers.mi.MIOptimalBall
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- 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
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Field Summary
Fields Modifier and Type Field Description static int
FILTER_NONE
No normalization/standardizationstatic int
FILTER_NORMALIZE
Normalize training datastatic int
FILTER_STANDARDIZE
Standardize training datastatic Tag[]
TAGS_FILTER
The filter to apply to the training data
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Constructor Summary
Constructors Constructor Description MIOptimalBall()
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description void
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 instancejava.lang.String
filterTypeTipText()
Returns the tip text for this propertyvoid
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 filterjava.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 bagvoid
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
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Field Detail
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FILTER_NORMALIZE
public static final int FILTER_NORMALIZE
Normalize training data- See Also:
- Constant Field Values
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FILTER_STANDARDIZE
public static final int FILTER_STANDARDIZE
Standardize training data- See Also:
- Constant Field Values
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FILTER_NONE
public static final int FILTER_NONE
No normalization/standardization- See Also:
- Constant Field Values
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TAGS_FILTER
public static final Tag[] TAGS_FILTER
The filter to apply to the training data
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Method Detail
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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
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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 interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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getCapabilities
public Capabilities getCapabilities()
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classClassifier
- Returns:
- the capabilities of this classifier
- See Also:
Capabilities
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getMultiInstanceCapabilities
public Capabilities 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:
Capabilities
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buildClassifier
public void buildClassifier(Instances data) throws java.lang.Exception
Builds the classifier- Specified by:
buildClassifier
in classClassifier
- 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
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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)
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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 spacebag
- the bag- Returns:
- the double value as the distance.
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findRadius
public void findRadius(Instances train)
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
public double[] distributionForInstance(Instance newBag) throws java.lang.Exception
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:
java.lang.Exception
- if the distribution can't be computed successfully
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listOptions
public java.util.Enumeration 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
public java.lang.String[] 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
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 interfaceOptionHandler
- Overrides:
setOptions
in classClassifier
- Parameters:
options
- the list of options as an array of strings- Throws:
java.lang.Exception
- if an option is not supported
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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
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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
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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
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getRevision
public java.lang.String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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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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