Package weka.classifiers.meta

Class Stacking

java.lang.Object
weka.classifiers.AbstractClassifier
weka.classifiers.MultipleClassifiersCombiner
weka.classifiers.ParallelMultipleClassifiersCombiner
weka.classifiers.RandomizableParallelMultipleClassifiersCombiner
weka.classifiers.meta.Stacking
All Implemented Interfaces:
Serializable, Cloneable, Classifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler
public class Stacking extends RandomizableParallelMultipleClassifiersCombiner implements TechnicalInformationHandler
Combines several classifiers using the stacking method. Can do classification or regression.

For more information, see

David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.

BibTeX: 

 @article{Wolpert1992,
    author = {David H. Wolpert},
    journal = {Neural Networks},
    pages = {241-259},
    publisher = {Pergamon Press},
    title = {Stacked generalization},
    volume = {5},
    year = {1992}
 }

Valid options are: 

 -M <scheme specification>
  Full name of meta classifier, followed by options.
  (default: "weka.classifiers.rules.Zero")
 -X <number of folds>
  Sets the number of cross-validation folds.
 -S <num>
  Random number seed.
  (default 1)
 -B <classifier specification>
  Full class name of classifier to include, followed
  by scheme options. May be specified multiple times.
  (default: "weka.classifiers.rules.ZeroR")
 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
Version:
$Revision: 15032 $
Author:
Eibe Frank (eibe@cs.waikato.ac.nz)
See Also:

  • Constructor Details

    • Stacking

      public Stacking()

  • Method Details

    • globalInfo

      public 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

    • listOptions

      public Enumeration<Option> listOptions()
      Returns an enumeration describing the available options.
      Specified by:
      listOptions in interface OptionHandler
      Overrides:
      listOptions in class RandomizableParallelMultipleClassifiersCombiner
      Returns:
      an enumeration of all the available options.

    • setOptions

      public void setOptions(String[] options) throws Exception
      Parses a given list of options.

      Valid options are: 

       -M <scheme specification>
  Full name of meta classifier, followed by options.
  (default: "weka.classifiers.rules.Zero")

       -X <number of folds>
  Sets the number of cross-validation folds.

       -S <num>
  Random number seed.
  (default 1)

       -B <classifier specification>
  Full class name of classifier to include, followed
  by scheme options. May be specified multiple times.
  (default: "weka.classifiers.rules.ZeroR")

       -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 RandomizableParallelMultipleClassifiersCombiner
      Parameters:
      options - the list of options as an array of strings
      Throws:
      Exception - if an option is not supported

    • getOptions

      public String[] getOptions()
      Gets the current settings of the Classifier.
      Specified by:
      getOptions in interface OptionHandler
      Overrides:
      getOptions in class RandomizableParallelMultipleClassifiersCombiner
      Returns:
      an array of strings suitable for passing to setOptions

    • numFoldsTipText

      public String numFoldsTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • getNumFolds

      public int getNumFolds()
      Gets the number of folds for the cross-validation.
      Returns:
      the number of folds for the cross-validation

    • setNumFolds

      public void setNumFolds(int numFolds) throws Exception
      Sets the number of folds for the cross-validation.
      Parameters:
      numFolds - the number of folds for the cross-validation
      Throws:
      Exception - if parameter illegal

    • metaClassifierTipText

      public String metaClassifierTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • setMetaClassifier

      public void setMetaClassifier(Classifier classifier)
      Adds meta classifier
      Parameters:
      classifier - the classifier with all options set.

    • getMetaClassifier

      public Classifier getMetaClassifier()
      Gets the meta classifier.
      Returns:
      the meta classifier

    • getCapabilities

      public Capabilities getCapabilities()
      Returns combined capabilities of the base classifiers, i.e., the capabilities all of them have in common.
      Specified by:
      getCapabilities in interface CapabilitiesHandler
      Specified by:
      getCapabilities in interface Classifier
      Overrides:
      getCapabilities in class MultipleClassifiersCombiner
      Returns:
      the capabilities of the base classifiers
      See Also:

    • implementsMoreEfficientBatchPrediction

      public boolean implementsMoreEfficientBatchPrediction()
      Returns true if the meta classifier or any of the base classifiers are able to generate batch predictions efficiently and all of them implement BatchPredictor.
      Specified by:
      implementsMoreEfficientBatchPrediction in interface BatchPredictor
      Overrides:
      implementsMoreEfficientBatchPrediction in class AbstractClassifier
      Returns:
      true if batch prediction can be done efficiently

    • baseClassifiersImplementMoreEfficientBatchPrediction

      public boolean baseClassifiersImplementMoreEfficientBatchPrediction()
      Returns true if any of the base classifiers are able to generate batch predictions efficiently and all of them implement BatchPredictor.
      Returns:
      true if the base classifiers can do batch prediction efficiently

    • buildClassifier

      public void buildClassifier(Instances data) throws Exception
      Builds a classifier using stacking. The base classifiers' output is fed into the meta classifier to make the final decision. The training data for the meta classifier is generated using (stratified) cross-validation.
      Specified by:
      buildClassifier in interface Classifier
      Overrides:
      buildClassifier in class ParallelMultipleClassifiersCombiner
      Parameters:
      data - the training data to be used for generating the stacked classifier.
      Throws:
      Exception - if the classifier could not be built successfully

    • distributionForInstance

      public double[] distributionForInstance(Instance instance) throws Exception
      Returns estimated class probabilities for the given instance if the class is nominal and a one-element array containing the numeric prediction if the class is numeric.
      Specified by:
      distributionForInstance in interface Classifier
      Overrides:
      distributionForInstance in class AbstractClassifier
      Parameters:
      instance - the instance to be classified
      Returns:
      the distribution
      Throws:
      Exception - if instance could not be classified successfully

    • distributionsForInstances

      public double[][] distributionsForInstances(Instances instances) throws Exception
      Returns class probabilities for all given instances if the class is nominal or corresponding predicted numeric values if the class is numeric. The meta classifier must implement BatchPredictor, otherwise an exception will be thrown.
      Specified by:
      distributionsForInstances in interface BatchPredictor
      Overrides:
      distributionsForInstances in class AbstractClassifier
      Parameters:
      instances - the instance sto be classified
      Returns:
      the distributions
      Throws:
      Exception - if instances could not be classified successfully

    • toString

      public String toString()
      Output a representation of this classifier
      Overrides:
      toString in class Object
      Returns:
      a string representation of the classifier

    • preExecution

      public void preExecution() throws Exception
      Description copied from class: AbstractClassifier
      Perform any setup stuff that might need to happen before commandline execution. Subclasses should override if they need to do something here
      Specified by:
      preExecution in interface CommandlineRunnable
      Overrides:
      preExecution in class MultipleClassifiersCombiner
      Throws:
      Exception - if a problem occurs during setup

    • postExecution

      public void postExecution() throws Exception
      Description copied from class: AbstractClassifier
      Perform any teardown stuff that might need to happen after execution. Subclasses should override if they need to do something here
      Specified by:
      postExecution in interface CommandlineRunnable
      Overrides:
      postExecution in class MultipleClassifiersCombiner
      Throws:
      Exception - if a problem occurs during teardown

    • getRevision

      public 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(String[] argv)
      Main method for testing this class.
      Parameters:
      argv - should contain the following arguments: -t training file [-T test file] [-c class index]