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moa.classifiers.meta.OzaBoost Class Reference

Incremental on-line boosting of Oza and Russell. More...

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List of all members.

Public Member Functions

String getPurposeString ()
 Gets the purpose of this object.
void resetLearningImpl ()
 Resets this classifier.
void trainOnInstanceImpl (Instance inst)
 Trains this classifier incrementally using the given instance.
double[] getVotesForInstance (Instance inst)
 Predicts the class memberships for a given instance.
boolean isRandomizable ()
 Gets whether this classifier needs a random seed.
void getModelDescription (StringBuilder out, int indent)
 Returns a string representation of the model.
Classifier[] getSubClassifiers ()
 Gets the classifiers of this ensemble.

Public Attributes

ClassOption baseLearnerOption
IntOption ensembleSizeOption
FlagOption pureBoostOption

Protected Member Functions

double getEnsembleMemberWeight (int i)
Measurement[] getModelMeasurementsImpl ()
 Gets the current measurements of this classifier.

Protected Attributes

Classifier[] ensemble
double[] scms
double[] swms

Detailed Description

Incremental on-line boosting of Oza and Russell.

See details in:
N. Oza and S. Russell. Online bagging and boosting. In Artificial Intelligence and Statistics 2001, pages 105–112. Morgan Kaufmann, 2001.

For the boosting method, Oza and Russell note that the weighting procedure of AdaBoost actually divides the total example weight into two halves – half of the weight is assigned to the correctly classified examples, and the other half goes to the misclassified examples. They use the Poisson distribution for deciding the random probability that an example is used for training, only this time the parameter changes according to the boosting weight of the example as it is passed through each model in sequence.

Parameters:

  • -l : Classifier to train
  • -s : The number of models to boost
  • -p : Boost with weights only; no poisson
Author:
Richard Kirkby (rkirkby@cs.waikato.ac.nz)
Version:
Revision:
7

Definition at line 54 of file OzaBoost.java.


Member Function Documentation

double moa.classifiers.meta.OzaBoost.getEnsembleMemberWeight ( int  i) [protected]

Definition at line 110 of file OzaBoost.java.

Referenced by moa.classifiers.meta.OzaBoost.getVotesForInstance().

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void moa.classifiers.meta.OzaBoost.getModelDescription ( StringBuilder  out,
int  indent 
) [virtual]

Returns a string representation of the model.

Parameters:
outthe stringbuilder to add the description
indentthe number of characters to indent

Implements moa.classifiers.AbstractClassifier.

Definition at line 142 of file OzaBoost.java.

Measurement [] moa.classifiers.meta.OzaBoost.getModelMeasurementsImpl ( ) [protected, virtual]

Gets the current measurements of this classifier.



The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods. Note that this will produce compiler errors if not overridden.

Returns:
an array of measurements to be used in evaluation tasks

Implements moa.classifiers.AbstractClassifier.

Definition at line 147 of file OzaBoost.java.

String moa.classifiers.meta.OzaBoost.getPurposeString ( )

Gets the purpose of this object.

Returns:
the string with the purpose of this object

Reimplemented from moa.classifiers.AbstractClassifier.

Definition at line 59 of file OzaBoost.java.

Classifier [] moa.classifiers.meta.OzaBoost.getSubClassifiers ( )

Gets the classifiers of this ensemble.

Returns null if this classifier is a single classifier.

Returns:
an array of the classifiers of the ensemble

Reimplemented from moa.classifiers.AbstractClassifier.

Definition at line 153 of file OzaBoost.java.

double [] moa.classifiers.meta.OzaBoost.getVotesForInstance ( Instance  inst)

Predicts the class memberships for a given instance.

If an instance is unclassified, the returned array elements must be all zero.

Parameters:
instthe instance to be classified
Returns:
an array containing the estimated membership probabilities of the test instance in each class

Implements moa.classifiers.Classifier.

Definition at line 119 of file OzaBoost.java.

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boolean moa.classifiers.meta.OzaBoost.isRandomizable ( )

Gets whether this classifier needs a random seed.

Examples of methods that needs a random seed are bagging and boosting.

Returns:
true if the classifier needs a random seed.

Implements moa.classifiers.Classifier.

Definition at line 137 of file OzaBoost.java.

void moa.classifiers.meta.OzaBoost.resetLearningImpl ( ) [virtual]

Resets this classifier.

It must be similar to starting a new classifier from scratch.

The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods. Note that this will produce compiler errors if not overridden.

Implements moa.classifiers.AbstractClassifier.

Definition at line 79 of file OzaBoost.java.

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void moa.classifiers.meta.OzaBoost.trainOnInstanceImpl ( Instance  inst) [virtual]

Trains this classifier incrementally using the given instance.



The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods. Note that this will produce compiler errors if not overridden.

Parameters:
instthe instance to be used for training

Implements moa.classifiers.AbstractClassifier.

Definition at line 91 of file OzaBoost.java.

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Member Data Documentation

Initial value:
 new ClassOption("baseLearner", 'l',
            "Classifier to train.", Classifier.class, "trees.HoeffdingTree")

Definition at line 63 of file OzaBoost.java.

Referenced by moa.classifiers.meta.OzaBoost.resetLearningImpl().

Initial value:
 new IntOption("ensembleSize", 's',
            "The number of models to boost.", 10, 1, Integer.MAX_VALUE)

Definition at line 66 of file OzaBoost.java.

Referenced by moa.classifiers.meta.OzaBoost.resetLearningImpl().

Initial value:
 new FlagOption("pureBoost", 'p',
            "Boost with weights only; no poisson.")

Definition at line 69 of file OzaBoost.java.

Referenced by moa.classifiers.meta.OzaBoost.trainOnInstanceImpl().


The documentation for this class was generated from the following file:
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