Package weka.classifiers.mi
Class MIDD
- java.lang.Object
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- weka.classifiers.Classifier
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- weka.classifiers.mi.MIDD
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- All Implemented Interfaces:
java.io.Serializable
,java.lang.Cloneable
,CapabilitiesHandler
,MultiInstanceCapabilitiesHandler
,OptionHandler
,RevisionHandler
,TechnicalInformationHandler
public class MIDD extends Classifier implements OptionHandler, MultiInstanceCapabilitiesHandler, TechnicalInformationHandler
Re-implement the Diverse Density algorithm, changes the testing procedure.
Oded Maron (1998). Learning from ambiguity.
O. Maron, T. Lozano-Perez (1998). A Framework for Multiple Instance Learning. Neural Information Processing Systems. 10. BibTeX:@phdthesis{Maron1998, author = {Oded Maron}, school = {Massachusetts Institute of Technology}, title = {Learning from ambiguity}, year = {1998} } @article{Maron1998, author = {O. Maron and T. Lozano-Perez}, journal = {Neural Information Processing Systems}, title = {A Framework for Multiple Instance Learning}, volume = {10}, year = {1998} }
Valid options are:-D Turn on debugging output.
-N <num> Whether to 0=normalize/1=standardize/2=neither. (default 1=standardize)
- Version:
- $Revision: 9144 $
- Author:
- Eibe Frank (eibe@cs.waikato.ac.nz), Xin Xu (xx5@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 MIDD()
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description void
buildClassifier(Instances train)
Builds the classifierdouble[]
distributionForInstance(Instance exmp)
Computes the distribution for a given exemplarjava.lang.String
filterTypeTipText()
Returns the tip text for this propertyCapabilities
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 optionsstatic void
main(java.lang.String[] argv)
Main method for testing this class.void
setFilterType(SelectedTag newType)
Sets how the training data will be transformed.void
setOptions(java.lang.String[] options)
Parses a given list of options.java.lang.String
toString()
Gets a string describing the classifier.-
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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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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setOptions
public void setOptions(java.lang.String[] options) throws java.lang.Exception
Parses a given list of options. Valid options are:-D Turn on debugging output.
-N <num> Whether to 0=normalize/1=standardize/2=neither. (default 1=standardize)
- 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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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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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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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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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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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 train) throws java.lang.Exception
Builds the classifier- Specified by:
buildClassifier
in classClassifier
- Parameters:
train
- 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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distributionForInstance
public double[] distributionForInstance(Instance exmp) throws java.lang.Exception
Computes the distribution for a given exemplar- Overrides:
distributionForInstance
in classClassifier
- Parameters:
exmp
- the exemplar for which distribution is computed- Returns:
- the distribution
- Throws:
java.lang.Exception
- if the distribution can't be computed successfully
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toString
public java.lang.String toString()
Gets a string describing the classifier.- Overrides:
toString
in classjava.lang.Object
- Returns:
- a string describing the classifer built.
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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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