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Class weka.classifiers.meta.StackingC

java.lang.Object
    |
    +----weka.classifiers.Classifier
            |
            +----weka.classifiers.DistributionClassifier
                    |
                    +----weka.classifiers.meta.StackingC

public class StackingC

extends DistributionClassifier

implements OptionHandler

Seewald A.K.: How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness, in Sammut C., Hoffmann A. (eds.), Proceedings of the Nineteenth International Conference on Machine Learning (ICML 2002), Morgan Kaufmann Publishers, pp.554-561, 2002.

Valid options are:

-X num_folds
The number of folds for the cross-validation (default 10).

-S seed
Random number seed (default 1).

-B classifierstring
Classifierstring should contain the full class name of a base scheme followed by options to the classifier. (required, option should be used once for each classifier).

-M classifierstring
Classifierstring for the meta classifier. Same format as for base classifiers. Has to be a function classifier, defaults to Linear Regression as in the original paper.

Version:

$Revision: 1.1 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz)

Author:

Alexander K. Seewald (alex@seewald.at)

StackingC()

 

buildClassifier(Instances)

Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.

distributionForInstance(Instance)

Classifies a given instance using the stacked classifier.

getBaseClassifier(int)

Gets the specific classifier from the set of base classifiers.

getBaseClassifiers()

Gets the list of possible classifers to choose from.

getMetaClassifier()

Gets the meta classifier.

getNumFolds()

Gets the number of folds for the cross-validation.

getOptions()

Gets the current settings of the Classifier.

getSeed()

Gets the random number seed.

listOptions()

Returns an enumeration describing the available options.

main(String[])

Main method for testing this class.

setBaseClassifiers(Classifier[])

Sets the list of possible classifers to choose from.

setMetaClassifier(Classifier)

Adds meta classifier

setNumFolds(int)

Sets the number of folds for the cross-validation.

setOptions(String[])

Parses a given list of options.

setSeed(int)

Sets the seed for random number generation.

toString()

Output a representation of this classifier

public StackingC()

public java.util.Enumeration listOptions()

          Returns an enumeration describing the available options.

Returns:

an enumeration of all the available options.

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

          Parses a given list of options. Valid options are:

-X num_folds
The number of folds for the cross-validation (default 10).

-S seed
Random number seed (default 1).

-B classifierstring
Classifierstring should contain the full class name of a base scheme followed by options to the classifier. (required, option should be used once for each classifier).

-M classifierstring
Classifierstring for the meta classifier. Same format as for base classifiers. Must be a function classifier. Default: Linear Regression

Parameters:

options - the list of options as an array of strings

Throws:

java.lang.Exception - if an option is not supported

public java.lang.String[] getOptions()

          Gets the current settings of the Classifier.

Returns:

an array of strings suitable for passing to setOptions

public void setSeed(int seed)

          Sets the seed for random number generation.

Parameters:

seed - the random number seed

public int getSeed()

          Gets the random number seed.

Returns:

the random number seed

public int getNumFolds()

          Gets the number of folds for the cross-validation.

Returns:

the number of folds for the cross-validation

public void setNumFolds(int numFolds) throws java.lang.Exception

          Sets the number of folds for the cross-validation.

Parameters:

numFolds - the number of folds for the cross-validation

Throws:

java.lang.Exception - if parameter illegal

public void setBaseClassifiers(Classifier classifiers[])

          Sets the list of possible classifers to choose from.

Parameters:

classifiers - an array of classifiers with all options set.

public Classifier[] getBaseClassifiers()

          Gets the list of possible classifers to choose from.

Returns:

the array of Classifiers

public Classifier getBaseClassifier(int index)

          Gets the specific classifier from the set of base classifiers.

Parameters:

index - the index of the classifier to retrieve

Returns:

the classifier

public void setMetaClassifier(Classifier classifier)

          Adds meta classifier

Parameters:

classifier - the classifier with all options set.

public Classifier getMetaClassifier()

          Gets the meta classifier.

Returns:

the meta classifier

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

          Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.

Parameters:

data - the training data to be used for generating the boosted classifier.

Throws:

java.lang.Exception - if the classifier could not be built successfully

Overrides:

buildClassifier in class Classifier

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

          Classifies a given instance using the stacked classifier.

Parameters:

instance - the instance to be classified

Throws:

java.lang.Exception - if instance could not be classified successfully

Overrides:

distributionForInstance in class DistributionClassifier

public java.lang.String toString()

          Output a representation of this classifier

Overrides:

toString in class java.lang.Object

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

          Main method for testing this class.

Parameters:

argv - should contain the following arguments: -t training file [-T test file] [-c class index]

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