weka.classifiers.meta

Class ClassificationViaRegression

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.SingleClassifierEnhancer

weka.classifiers.meta.ClassificationViaRegression

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class ClassificationViaRegression
extends SingleClassifierEnhancer
implements TechnicalInformationHandler, WeightedInstancesHandler

Class for doing classification using regression methods. Class is binarized and one regression model is built for each class value. For more information, see, for example

E. Frank, Y. Wang, S. Inglis, G. Holmes, I.H. Witten (1998). Using model trees for classification. Machine Learning. 32(1):63-76.

BibTeX:

 @article{Frank1998,
    author = {E. Frank and Y. Wang and S. Inglis and G. Holmes and I.H. Witten},
    journal = {Machine Learning},
    number = {1},
    pages = {63-76},
    title = {Using model trees for classification},
    volume = {32},
    year = {1998}
 }
 

Valid options are:

 -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.M5P)

 
 Options specific to classifier weka.classifiers.trees.M5P:
 

 -N
  Use unpruned tree/rules

 -U
  Use unsmoothed predictions

 -R
  Build regression tree/rule rather than a model tree/rule

 -M <minimum number of instances>
  Set minimum number of instances per leaf
  (default 4)

 -L
  Save instances at the nodes in
  the tree (for visualization purposes)

Version:

$Revision: 15482 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (trigg@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors

Constructor and Description
ClassificationViaRegression() Default constructor.

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(Instances insts) Builds the classifiers.
double[]	distributionForInstance(Instance inst) Returns the distribution for an instance.
double[][]	distributionsForInstances(Instances insts) Returns predictions for a whole set of instances.
Capabilities	getCapabilities() Returns default capabilities 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 classifier
boolean	implementsMoreEfficientBatchPrediction() Return whether this classifier configuration yields more efficient batch prediction
static void	main(java.lang.String[] argv) Main method for testing this class.
java.lang.String	toString() Prints the classifiers.

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, getClassifier, getOptions, listOptions, postExecution, preExecution, setClassifier, setOptions

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

ClassificationViaRegression

public ClassificationViaRegression()

Default constructor.

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing classifier

Returns:

a description 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

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface Classifier

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class SingleClassifierEnhancer

Returns:

the capabilities of this classifier

See Also:

Capabilities

buildClassifier

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

Builds the classifiers.

Specified by:

buildClassifier in interface Classifier

Parameters:

insts - the training data.

Throws:

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

distributionForInstance

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

Returns the distribution for an instance.

Specified by:

distributionForInstance in interface Classifier

Overrides:

distributionForInstance in class AbstractClassifier

Parameters:

inst - the instance to get the distribution for

Returns:

the computed distribution

Throws:

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

implementsMoreEfficientBatchPrediction

public boolean implementsMoreEfficientBatchPrediction()

Return whether this classifier configuration yields more efficient batch prediction

Specified by:

implementsMoreEfficientBatchPrediction in interface BatchPredictor

Overrides:

implementsMoreEfficientBatchPrediction in class AbstractClassifier

Returns:

the base classifier's flag indicating whether it can do batch prediction efficiently

distributionsForInstances

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

Returns predictions for a whole set of instances.

Specified by:

distributionsForInstances in interface BatchPredictor

Overrides:

distributionsForInstances in class AbstractClassifier

Parameters:

insts - the instances to make predictions for

Returns:

the 2D array with results

Throws:

java.lang.Exception - if a problem occurs.

toString

public java.lang.String toString()

Prints the classifiers.

Overrides:

toString in class java.lang.Object

Returns:

a string representation of the classifier

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class AbstractClassifier

Returns:

the revision

main

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

Main method for testing this class.

Parameters:

argv - the options for the learner