weka.classifiers.mi
Class MIEMDD

java.lang.Object
  extended by weka.classifiers.Classifier
      extended by weka.classifiers.RandomizableClassifier
          extended by weka.classifiers.mi.MIEMDD
All Implemented Interfaces:
java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, MultiInstanceCapabilitiesHandler, OptionHandler, Randomizable, TechnicalInformationHandler

public class MIEMDD
extends RandomizableClassifier
implements OptionHandler, MultiInstanceCapabilitiesHandler, TechnicalInformationHandler

EMDD model builds heavily upon Dietterich's Diverse Density (DD) algorithm.
It is a general framework for MI learning of converting the MI problem to a single-instance setting using EM. In this implementation, we use most-likely cause DD model and only use 3 random selected postive bags as initial starting points of EM.

For more information see:

Qi Zhang, Sally A. Goldman: EM-DD: An Improved Multiple-Instance Learning Technique. In: Advances in Neural Information Processing Systems 14, 1073-108, 2001.

BibTeX:

 @inproceedings{Zhang2001,
    author = {Qi Zhang and Sally A. Goldman},
    booktitle = {Advances in Neural Information Processing Systems 14},
    pages = {1073-108},
    publisher = {MIT Press},
    title = {EM-DD: An Improved Multiple-Instance Learning Technique},
    year = {2001}
 }
 

Valid options are:

 -N <num>
  Whether to 0=normalize/1=standardize/2=neither.
  (default 1=standardize)
 -S <num>
  Random number seed.
  (default 1)
 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

Version:
$Revision: 1.5 $
Author:
Eibe Frank (eibe@cs.waikato.ac.nz), Lin Dong (ld21@cs.waikato.ac.nz)
See Also:
Serialized Form

Field Summary
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
MIEMDD()
           
 
Method Summary
 void buildClassifier(Instances train)
          Builds the classifier
 double[] distributionForInstance(Instance exmp)
          Computes the distribution for a given exemplar
 java.lang.String filterTypeTipText()
          Returns the tip text for this property
 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.
 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.
 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.RandomizableClassifier
getSeed, seedTipText, setSeed
 
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, 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

MIEMDD

public MIEMDD()
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

listOptions

public java.util.Enumeration listOptions()
Returns an enumeration describing the available options

Specified by:
listOptions in interface OptionHandler
Overrides:
listOptions in class RandomizableClassifier
Returns:
an enumeration of all the available options

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 1=standardize)
 -S <num>
  Random number seed.
  (default 1)
 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

Specified by:
setOptions in interface OptionHandler
Overrides:
setOptions in class RandomizableClassifier
Parameters:
options - the list of options as an array of strings
Throws:
java.lang.Exception - if an option is not supported

getOptions

public java.lang.String[] getOptions()
Gets the current settings of the classifier.

Specified by:
getOptions in interface OptionHandler
Overrides:
getOptions in class RandomizableClassifier
Returns:
an array of strings suitable for passing to setOptions

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

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

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

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 train)
                     throws java.lang.Exception
Builds the classifier

Specified by:
buildClassifier in class Classifier
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

distributionForInstance

public double[] distributionForInstance(Instance exmp)
                                 throws java.lang.Exception
Computes the distribution for a given exemplar

Overrides:
distributionForInstance in class Classifier
Parameters:
exmp - the exemplar for which distribution is computed
Returns:
the distribution
Throws:
java.lang.Exception - if the distribution can't be computed successfully

toString

public java.lang.String toString()
Gets a string describing the classifier.

Overrides:
toString in class java.lang.Object
Returns:
a string describing the classifer built.

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)