Package weka.classifiers.meta

Class AdaBoostM1

  • All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, Classifier, IterativeClassifier, Sourcable, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler
    public class AdaBoostM1
extends RandomizableIteratedSingleClassifierEnhancer
implements WeightedInstancesHandler, Sourcable, TechnicalInformationHandler, IterativeClassifier
    Class for boosting a nominal class classifier using the Adaboost M1 method. Only nominal class problems can be tackled. Often dramatically improves performance, but sometimes overfits.

    For more information, see

    Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.

    BibTeX: 

     @inproceedings{Freund1996,
    address = {San Francisco},
    author = {Yoav Freund and Robert E. Schapire},
    booktitle = {Thirteenth International Conference on Machine Learning},
    pages = {148-156},
    publisher = {Morgan Kaufmann},
    title = {Experiments with a new boosting algorithm},
    year = {1996}
 }

    Valid options are: 

     -P <num>
  Percentage of weight mass to base training on.
  (default 100, reduce to around 90 speed up)
 
     -Q
  Use resampling for boosting.
 
     -S <num>
  Random number seed.
  (default 1)
 
     -I <num>
  Number of iterations.
  (default 10)
 
     -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.DecisionStump)
 
     Options specific to classifier weka.classifiers.trees.DecisionStump:
 
     -D
  If set, classifier is run in debug mode and
  may output additional info to the console
    Options after -- are passed to the designated classifier.

    Version:
    $Revision: 15022 $
    Author:
    Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (trigg@cs.waikato.ac.nz)
    See Also:
    Serialized Form

    • Constructor Detail

      • AdaBoostM1

        public AdaBoostM1()
        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

      • setOptions

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

        Valid options are: 

         -P <num>
  Percentage of weight mass to base training on.
  (default 100, reduce to around 90 speed up)
 
         -Q
  Use resampling for boosting.
 
         -S <num>
  Random number seed.
  (default 1)
 
         -I <num>
  Number of iterations.
  (default 10)
 
         -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.DecisionStump)
 
         Options specific to classifier weka.classifiers.trees.DecisionStump:
 
         -D
  If set, classifier is run in debug mode and
  may output additional info to the console
        Options after -- are passed to the designated classifier.

        Specified by:
        setOptions in interface OptionHandler
        Overrides:
        setOptions in class RandomizableIteratedSingleClassifierEnhancer
        Parameters:
        options - the list of options as an array of strings
        Throws:
        java.lang.Exception - if an option is not supported

      • weightThresholdTipText

        public java.lang.String weightThresholdTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • setWeightThreshold

        public void setWeightThreshold​(int threshold)
        Set weight threshold
        Parameters:
        threshold - the percentage of weight mass used for training

      • getWeightThreshold

        public int getWeightThreshold()
        Get the degree of weight thresholding
        Returns:
        the percentage of weight mass used for training

      • useResamplingTipText

        public java.lang.String useResamplingTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • setUseResampling

        public void setUseResampling​(boolean r)
        Set resampling mode
        Parameters:
        r - true if resampling should be done

      • getUseResampling

        public boolean getUseResampling()
        Get whether resampling is turned on
        Returns:
        true if resampling output is on

      • buildClassifier

        public void buildClassifier​(Instances data)
                     throws java.lang.Exception
        Method used to build the classifier.
        Specified by:
        buildClassifier in interface Classifier
        Overrides:
        buildClassifier in class IteratedSingleClassifierEnhancer
        Parameters:
        data - the training data to be used for generating the bagged classifier.
        Throws:
        java.lang.Exception - if the classifier could not be built successfully

      • initializeClassifier

        public void initializeClassifier​(Instances data)
                          throws java.lang.Exception
        Initialize the classifier.
        Specified by:
        initializeClassifier in interface IterativeClassifier
        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

      • next

        public boolean next()
             throws java.lang.Exception
        Perform the next boosting iteration.
        Specified by:
        next in interface IterativeClassifier
        Returns:
        false if no further iterations could be performed, true otherwise
        Throws:
        java.lang.Exception - if an unforeseen problem occurs

      • resumeTipText

        public java.lang.String resumeTipText()
        Tool tip text for the resume property
        Returns:
        the tool tip text for the finalize property

      • setResume

        public void setResume​(boolean resume)
        If called with argument true, then the next time done() is called the model is effectively "frozen" and no further iterations can be performed
        Specified by:
        setResume in interface IterativeClassifier
        Parameters:
        resume - true if the model is to be finalized after performing iterations

      • getResume

        public boolean getResume()
        Returns true if the model is to be finalized (or has been finalized) after training.
        Specified by:
        getResume in interface IterativeClassifier
        Returns:
        the current value of finalize

      • distributionForInstance

        public double[] distributionForInstance​(Instance instance)
                                 throws java.lang.Exception
        Calculates the class membership probabilities for the given test instance.
        Specified by:
        distributionForInstance in interface Classifier
        Overrides:
        distributionForInstance in class AbstractClassifier
        Parameters:
        instance - the instance to be classified
        Returns:
        predicted class probability distribution
        Throws:
        java.lang.Exception - if instance could not be classified successfully

      • toSource

        public java.lang.String toSource​(java.lang.String className)
                          throws java.lang.Exception
        Returns the boosted model as Java source code.
        Specified by:
        toSource in interface Sourcable
        Parameters:
        className - the classname of the generated class
        Returns:
        the tree as Java source code
        Throws:
        java.lang.Exception - if something goes wrong

      • toString

        public java.lang.String toString()
        Returns description of the boosted classifier.
        Overrides:
        toString in class java.lang.Object
        Returns:
        description of the boosted classifier as a string

      • main

        public static void main​(java.lang.String[] argv)
        Main method for testing this class.
        Parameters:
        argv - the options