Package weka.classifiers.mi
Class MISVM
java.lang.Object
weka.classifiers.Classifier
weka.classifiers.mi.MISVM
- All Implemented Interfaces:
Serializable
,Cloneable
,CapabilitiesHandler
,MultiInstanceCapabilitiesHandler
,OptionHandler
,RevisionHandler
,TechnicalInformationHandler
public class MISVM
extends Classifier
implements OptionHandler, MultiInstanceCapabilitiesHandler, TechnicalInformationHandler
Implements Stuart Andrews' mi_SVM (Maximum pattern Margin Formulation of MIL). Applying weka.classifiers.functions.SMO to solve multiple instances problem.
The algorithm first assign the bag label to each instance in the bag as its initial class label. After that applying SMO to compute SVM solution for all instances in positive bags And then reassign the class label of each instance in the positive bag according to the SVM result Keep on iteration until labels do not change anymore.
For more information see:
Stuart Andrews, Ioannis Tsochantaridis, Thomas Hofmann: Support Vector Machines for Multiple-Instance Learning. In: Advances in Neural Information Processing Systems 15, 561-568, 2003. BibTeX:
The algorithm first assign the bag label to each instance in the bag as its initial class label. After that applying SMO to compute SVM solution for all instances in positive bags And then reassign the class label of each instance in the positive bag according to the SVM result Keep on iteration until labels do not change anymore.
For more information see:
Stuart Andrews, Ioannis Tsochantaridis, Thomas Hofmann: Support Vector Machines for Multiple-Instance Learning. In: Advances in Neural Information Processing Systems 15, 561-568, 2003. BibTeX:
@inproceedings{Andrews2003, author = {Stuart Andrews and Ioannis Tsochantaridis and Thomas Hofmann}, booktitle = {Advances in Neural Information Processing Systems 15}, pages = {561-568}, publisher = {MIT Press}, title = {Support Vector Machines for Multiple-Instance Learning}, year = {2003} }Valid options are:
-D If set, classifier is run in debug mode and may output additional info to the console
-C <double> The complexity constant C. (default 1)
-N <default 0> Whether to 0=normalize/1=standardize/2=neither. (default: 0=normalize)
-I <num> The maximum number of iterations to perform. (default: 500)
-K <classname and parameters> The Kernel to use. (default: weka.classifiers.functions.supportVector.PolyKernel)
Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:
-D Enables debugging output (if available) to be printed. (default: off)
-no-checks Turns off all checks - use with caution! (default: checks on)
-C <num> The size of the cache (a prime number), 0 for full cache and -1 to turn it off. (default: 250007)
-E <num> The Exponent to use. (default: 1.0)
-L Use lower-order terms. (default: no)
- 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 train) Builds the classifiercTipText()
Returns the tip text for this propertydouble[]
Computes the distribution for a given exemplarReturns the tip text for this propertydouble
getC()
Get the value of C.Returns default capabilities of the classifier.Gets how the training data will be transformed.Gets the kernel to use.int
Gets the maximum number of iterations.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 the tip text for this propertyReturns an enumeration describing the available optionsstatic void
Main method for testing this class.Returns the tip text for this propertyvoid
setC
(double v) Set the value of C.void
setFilterType
(SelectedTag newType) Sets how the training data will be transformed.void
Sets the kernel to use.void
setMaxIterations
(int value) Sets the maximum number of iterations.void
setOptions
(String[] options) Parses a given list of options.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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MISVM
public MISVM()
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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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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
Parses a given list of options. Valid options are:-D If set, classifier is run in debug mode and may output additional info to the console
-C <double> The complexity constant C. (default 1)
-N <default 0> Whether to 0=normalize/1=standardize/2=neither. (default: 0=normalize)
-I <num> The maximum number of iterations to perform. (default: 500)
-K <classname and parameters> The Kernel to use. (default: weka.classifiers.functions.supportVector.PolyKernel)
Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:
-D Enables debugging output (if available) to be printed. (default: off)
-no-checks Turns off all checks - use with caution! (default: checks on)
-C <num> The size of the cache (a prime number), 0 for full cache and -1 to turn it off. (default: 250007)
-E <num> The Exponent to use. (default: 1.0)
-L Use lower-order terms. (default: no)
- 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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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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kernelTipText
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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getKernel
Gets the kernel to use.- Returns:
- the kernel
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setKernel
Sets the kernel to use.- Parameters:
value
- the kernel
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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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cTipText
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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getC
public double getC()Get the value of C.- Returns:
- Value of C.
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setC
public void setC(double v) Set the value of C.- Parameters:
v
- Value to assign to C.
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maxIterationsTipText
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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getMaxIterations
public int getMaxIterations()Gets the maximum number of iterations.- Returns:
- the maximum number of iterations.
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setMaxIterations
public void setMaxIterations(int value) Sets the maximum number of iterations.- Parameters:
value
- the maximum number of iterations.
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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:
train
- 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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distributionForInstance
Computes the distribution for a given exemplar- Overrides:
distributionForInstance
in classClassifier
- Parameters:
exmp
- the exemplar for which distribution is computed- Returns:
- the distribution
- Throws:
Exception
- if the distribution can't be computed successfully
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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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