weka.classifiers.meta

Class OrdinalClassClassifier

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.SingleClassifierEnhancer

weka.classifiers.meta.OrdinalClassClassifier

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, weka.classifiers.Classifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler

public class OrdinalClassClassifier
extends weka.classifiers.SingleClassifierEnhancer
implements weka.core.OptionHandler, weka.core.TechnicalInformationHandler

Meta classifier that allows standard classification algorithms to be applied to ordinal class problems.

For more information see:

Eibe Frank, Mark Hall: A Simple Approach to Ordinal Classification. In: 12th European Conference on Machine Learning, 145-156, 2001.

Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, Janos A. Csirik: Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation. In: Machine Learning, Proceedings of the Nineteenth International Conference (ICML 2002), 546-553, 2002.

BibTeX:

 @inproceedings{Frank2001,
    author = {Eibe Frank and Mark Hall},
    booktitle = {12th European Conference on Machine Learning},
    pages = {145-156},
    publisher = {Springer},
    title = {A Simple Approach to Ordinal Classification},
    year = {2001}
 }
 
 @inproceedings{Schapire2002,
    author = {Robert E. Schapire and Peter Stone and David A. McAllester and Michael L. Littman and Janos A. Csirik},
    booktitle = {Machine Learning, Proceedings of the Nineteenth International Conference (ICML 2002)},
    pages = {546-553},
    publisher = {Morgan Kaufmann},
    title = {Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation},
    year = {2002}
 }
 

Valid options are:

 -S
  Turn off Schapire et al.'s smoothing heuristic (ICML02, pp. 550).
 

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
 

 -W
  Full name of base classifier.
  (default: weka.classifiers.trees.J48)
 

 Options specific to classifier weka.classifiers.trees.J48:
 

 -U
  Use unpruned tree.
 

 -C <pruning confidence>
  Set confidence threshold for pruning.
  (default 0.25)
 

 -M <minimum number of instances>
  Set minimum number of instances per leaf.
  (default 2)
 

 -R
  Use reduced error pruning.
 

 -N <number of folds>
  Set number of folds for reduced error
  pruning. One fold is used as pruning set.
  (default 3)
 

 -B
  Use binary splits only.
 

 -S
  Don't perform subtree raising.
 

 -L
  Do not clean up after the tree has been built.
 

 -A
  Laplace smoothing for predicted probabilities.
 

 -Q <seed>
  Seed for random data shuffling (default 1).
 

Version:

$Revision: 14575 $

Author:

Mark Hall, Eibe Frank

See Also:

OptionHandler, Serialized Form

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors

Constructor and Description
OrdinalClassClassifier() Default constructor.

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(weka.core.Instances insts) Builds the classifiers.
double[]	distributionForInstance(weka.core.Instance inst) Returns the distribution for an instance.
double[][]	distributionsForInstances(weka.core.Instances insts) Returns the distributions for a set of instances.
weka.core.Capabilities	getCapabilities() Returns default capabilities of the classifier.
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
java.lang.String	getRevision() Returns the revision string.
weka.core.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.
boolean	getUseSmoothing() Checks whether Schapire et al.'s smoothing method is used.
java.lang.String	globalInfo() Returns a string describing this attribute evaluator
boolean	implementsMoreEfficientBatchPrediction() Returns true if the base classifier implements BatchPredictor and is able to generate batch predictions efficiently
java.util.Enumeration<weka.core.Option>	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] argv) Main method for testing this class.
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setUseSmoothing(boolean b) Determines whether Schapire et al.'s smoothing method is used.
java.lang.String	toString() Prints the classifiers.
java.lang.String	useSmoothingTipText() Tip text method.

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, getClassifier, postExecution, preExecution, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, classifyInstance, debugTipText, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.core.OptionHandler

makeCopy

Constructor Detail

OrdinalClassClassifier

public OrdinalClassClassifier()

Default constructor.

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing this attribute evaluator

Returns:

a description of the evaluator suitable for displaying in the explorer/experimenter gui

getTechnicalInformation

public weka.core.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 weka.core.TechnicalInformationHandler

Returns:

the technical information about this class

getCapabilities

public weka.core.Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface weka.classifiers.Classifier

Specified by:

getCapabilities in interface weka.core.CapabilitiesHandler

Overrides:

getCapabilities in class weka.classifiers.SingleClassifierEnhancer

Returns:

the capabilities of this classifier

buildClassifier

public void buildClassifier(weka.core.Instances insts)
                     throws java.lang.Exception

Builds the classifiers.

Specified by:

buildClassifier in interface weka.classifiers.Classifier

Parameters:

insts - the training data.

Throws:

java.lang.Exception - if a classifier can't be built

distributionForInstance

public double[] distributionForInstance(weka.core.Instance inst)
                                 throws java.lang.Exception

Returns the distribution for an instance.

Specified by:

distributionForInstance in interface weka.classifiers.Classifier

Overrides:

distributionForInstance in class weka.classifiers.AbstractClassifier

Parameters:

inst - the instance to compute the distribution for

Returns:

the class distribution for the given instance

Throws:

java.lang.Exception - if the distribution can't be computed successfully

implementsMoreEfficientBatchPrediction

public boolean implementsMoreEfficientBatchPrediction()

Returns true if the base classifier implements BatchPredictor and is able to generate batch predictions efficiently

Specified by:

implementsMoreEfficientBatchPrediction in interface weka.core.BatchPredictor

Overrides:

implementsMoreEfficientBatchPrediction in class weka.classifiers.AbstractClassifier

Returns:

true if the base classifier can generate batch predictions efficiently

distributionsForInstances

public double[][] distributionsForInstances(weka.core.Instances insts)
                                     throws java.lang.Exception

Returns the distributions for a set of instances. Calls base classifier's distributionForInstance() if it does not support distributionForInstances().

Specified by:

distributionsForInstances in interface weka.core.BatchPredictor

Overrides:

distributionsForInstances in class weka.classifiers.AbstractClassifier

Parameters:

insts - the instances to compute the distribution for

Returns:

the class distributions for the given instances

Throws:

java.lang.Exception - if the distribution can't be computed successfully

listOptions

public java.util.Enumeration<weka.core.Option> listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface weka.core.OptionHandler

Overrides:

listOptions in class weka.classifiers.SingleClassifierEnhancer

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:

 -S
  Turn off Schapire et al.'s smoothing heuristic (ICML02, pp. 550).
 

 -W
  Full name of base classifier.
  (default: weka.classifiers.trees.J48)
 

 Options specific to classifier weka.classifiers.trees.J48:
 

 -U
  Use unpruned tree.
 

 -C <pruning confidence>
  Set confidence threshold for pruning.
  (default 0.25)
 

 -M <minimum number of instances>
  Set minimum number of instances per leaf.
  (default 2)
 

 -R
  Use reduced error pruning.
 

 -N <number of folds>
  Set number of folds for reduced error
  pruning. One fold is used as pruning set.
  (default 3)
 

 -B
  Use binary splits only.
 

 -S
  Don't perform subtree raising.
 

 -L
  Do not clean up after the tree has been built.
 

 -A
  Laplace smoothing for predicted probabilities.
 

 -Q <seed>
  Seed for random data shuffling (default 1).
 

Specified by:

setOptions in interface weka.core.OptionHandler

Overrides:

setOptions in class weka.classifiers.SingleClassifierEnhancer

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 weka.core.OptionHandler

Overrides:

getOptions in class weka.classifiers.SingleClassifierEnhancer

Returns:

an array of strings suitable for passing to setOptions

useSmoothingTipText

public java.lang.String useSmoothingTipText()

Tip text method.

Returns:

a tip text string suitable for displaying as a popup in the GUI.

setUseSmoothing

public void setUseSmoothing(boolean b)

Determines whether Schapire et al.'s smoothing method is used.

Parameters:

b - true if the smoothing heuristic is to be used.

getUseSmoothing

public boolean getUseSmoothing()

Checks whether Schapire et al.'s smoothing method is used.

Returns:

true if the smoothing heuristic is to be used.

toString

public java.lang.String toString()

Prints the classifiers.

Overrides:

toString in class java.lang.Object

Returns:

a string representation of this classifier

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface weka.core.RevisionHandler

Overrides:

getRevision in class weka.classifiers.AbstractClassifier

Returns:

the revision

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

argv - the options