weka.classifiers
Class ClassificationViaRegression

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.DistributionClassifier
              |
              +--weka.classifiers.ClassificationViaRegression

All Implemented Interfaces:

java.lang.Cloneable, OptionHandler, java.io.Serializable

public class ClassificationViaRegression

extends DistributionClassifier

implements OptionHandler

Class for doing classification using regression methods. For more information, see

E. Frank, Y. Wang, S. Inglis, G. Holmes, and I.H. Witten (1998) "Using model trees for classification", Machine Learning, Vol.32, No.1, pp. 63-76.

Valid options are:

-W classname
Specify the full class name of a numeric predictor as the basis for the classifier (required).

Author:

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

See Also:

Serialized Form

Constructor Summary

ClassificationViaRegression()

Method Summary

void	buildClassifier(Instances insts) Builds the classifiers.
double[]	distributionForInstance(Instance inst) Returns the distribution for an instance.
Classifier	getClassifier() Get the base classifier (regression scheme) used as the classifier
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
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	setClassifier(Classifier newClassifier) Set the base classifier.
void	setOptions(java.lang.String[] options) Sets a given list of options.
java.lang.String	toString() Prints the classifiers.

Methods inherited from class weka.classifiers.DistributionClassifier

classifyInstance

Methods inherited from class weka.classifiers.Classifier

forName, makeCopies

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

ClassificationViaRegression

public ClassificationViaRegression()

Method Detail

buildClassifier

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

Builds the classifiers.

Overrides:

buildClassifier in class 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.

Overrides:

distributionForInstance in class DistributionClassifier

Throws:

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

toString

public java.lang.String toString()

Prints the classifiers.

Overrides:

toString in class java.lang.Object

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options

Specified by:

listOptions in interface OptionHandler

Returns:

an enumeration of all the available options

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Sets a given list of options. Valid options are:

-W classname
Specify the full class name of a numeric predictor as the basis for the classifier (required).

Specified by:

setOptions in interface OptionHandler

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

Returns:

an array of strings suitable for passing to setOptions

setClassifier

public void setClassifier(Classifier newClassifier)

Set the base classifier.

Parameters:

newClassifier - the Classifier to use.

getClassifier

public Classifier getClassifier()

Get the base classifier (regression scheme) used as the classifier

Returns:

the classifier used as the classifier

main

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

Main method for testing this class.

Parameters:

argv - the options for the learner