moa.classifiers.meta

Class AccuracyUpdatedEnsemble

java.lang.Object

moa.AbstractMOAObject

moa.options.AbstractOptionHandler

moa.classifiers.AbstractClassifier

moa.classifiers.meta.AccuracyUpdatedEnsemble

All Implemented Interfaces:

Serializable, Classifier, AWTRenderable, MOAObject, OptionHandler

public class AccuracyUpdatedEnsemble
extends AbstractClassifier

The revised version of the Accuracy Updated Ensemble as proposed by Brzezinski and Stefanowski in "Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm", IEEE Trans. Neural Netw, 2013.

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected Classifier	candidate Candidate classifier.
IntOption	chunkSizeOption Chunk size.
protected long[]	classDistributions Class distributions.
protected weka.core.Instances	currentChunk Current chunk of instances.
ClassOption	learnerOption Type of classifier to use as a component classifier.
protected Classifier[]	learners Ensemble classifiers.
IntOption	maxByteSizeOption Determines the maximum size of model (evaluated after every chunk).
IntOption	memberCountOption Number of component classifiers.
protected int	processedInstances Number of processed examples.
protected double[][]	weights The weights of stored classifiers.

Fields inherited from class moa.classifiers.AbstractClassifier

classifierRandom, modelContext, randomSeed, randomSeedOption, trainingWeightSeenByModel

Fields inherited from class moa.options.AbstractOptionHandler

classOptionNamesToPreparedObjects, options

Constructor Summary

Constructors

Constructor and Description
AccuracyUpdatedEnsemble()

Method Summary

Methods

Modifier and Type	Method and Description
protected Classifier	addToStored(Classifier newClassifier, double newClassifiersWeight) Adds a classifier to the storage.
protected double	computeMse(Classifier learner, weka.core.Instances chunk) Computes the MSE of a learner for a given chunk of examples.
protected double	computeMseR() Computes the MSEr threshold.
protected void	enforceMemoryLimit() Checks if the memory limit is exceeded and if so prunes the classifiers in the ensemble.
void	getModelDescription(StringBuilder out, int indent) Returns a string representation of the model.
protected Measurement[]	getModelMeasurementsImpl() Adds ensemble weights to the measurements.
Classifier[]	getSubClassifiers() Gets the classifiers of this ensemble.
double[]	getVotesForInstance(weka.core.Instance inst) Predicts a class for an example.
boolean	isRandomizable() Determines whether the classifier is randomizable.
void	prepareForUseImpl(TaskMonitor monitor, ObjectRepository repository) This method describes the implementation of how to prepare this object for use.
protected void	processChunk() Processes a chunk of instances.
void	resetLearningImpl() Resets this classifier.
void	trainOnInstanceImpl(weka.core.Instance inst) 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.

Methods inherited from class moa.classifiers.AbstractClassifier

contextIsCompatible, copy, correctlyClassifies, getAttributeNameString, getAWTRenderer, getClassLabelString, getClassNameString, getDescription, getModelContext, getModelMeasurements, getNominalValueString, getPurposeString, modelAttIndexToInstanceAttIndex, modelAttIndexToInstanceAttIndex, resetLearning, setModelContext, setRandomSeed, trainingHasStarted, trainingWeightSeenByModel, trainOnInstance

Methods inherited from class moa.options.AbstractOptionHandler

discoverOptionsViaReflection, getCLICreationString, getOptions, getPreparedClassOption, getPreparedClassOption, prepareClassOptions, prepareForUse, prepareForUse

Methods inherited from class moa.AbstractMOAObject

copy, measureByteSize, measureByteSize, toString

Methods inherited from class java.lang.Object

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

Methods inherited from interface moa.options.OptionHandler

getCLICreationString, getOptions, prepareForUse, prepareForUse

Methods inherited from interface moa.MOAObject

measureByteSize

Field Detail

learnerOption

public ClassOption learnerOption

Type of classifier to use as a component classifier.

memberCountOption

public IntOption memberCountOption

Number of component classifiers.

chunkSizeOption

public IntOption chunkSizeOption

Chunk size.

maxByteSizeOption

public IntOption maxByteSizeOption

Determines the maximum size of model (evaluated after every chunk).

weights

protected double[][] weights

The weights of stored classifiers. weights[x][0] = weight weights[x][1] = classifier number in learners

classDistributions

protected long[] classDistributions

Class distributions.

learners

protected Classifier[] learners

Ensemble classifiers.

processedInstances

protected int processedInstances

Number of processed examples.

candidate

protected Classifier candidate

Candidate classifier.

currentChunk

protected weka.core.Instances currentChunk

Current chunk of instances.

Constructor Detail

AccuracyUpdatedEnsemble

public AccuracyUpdatedEnsemble()

Method Detail

prepareForUseImpl

public void prepareForUseImpl(TaskMonitor monitor,
                     ObjectRepository repository)

Description copied from class: AbstractOptionHandler

This method describes the implementation of how to prepare this object for use. All classes that extends this class have to implement prepareForUseImpl and not prepareForUse since prepareForUse calls prepareForUseImpl.

Overrides:

prepareForUseImpl in class AbstractClassifier

Parameters:

monitor - the TaskMonitor to use

repository - the ObjectRepository to use

resetLearningImpl

public void resetLearningImpl()

Description copied from class: AbstractClassifier

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.

Specified by:

resetLearningImpl in class AbstractClassifier

trainOnInstanceImpl

public void trainOnInstanceImpl(weka.core.Instance inst)

Description copied from class: AbstractClassifier

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.

Specified by:

trainOnInstanceImpl in class AbstractClassifier

Parameters:

inst - the instance to be used for training

isRandomizable

public boolean isRandomizable()

Determines whether the classifier is randomizable.

Returns:

true if the classifier needs a random seed.

getVotesForInstance

public double[] getVotesForInstance(weka.core.Instance inst)

Predicts a class for an example.

Parameters:

inst - the instance to be classified

Returns:

an array containing the estimated membership probabilities of the test instance in each class

getModelDescription

public void getModelDescription(StringBuilder out,
                       int indent)

Description copied from class: AbstractClassifier

Returns a string representation of the model.

Specified by:

getModelDescription in class AbstractClassifier

Parameters:

out - the stringbuilder to add the description

indent - the number of characters to indent

getSubClassifiers

public Classifier[] getSubClassifiers()

Description copied from interface: Classifier

Gets the classifiers of this ensemble. Returns null if this classifier is a single classifier.

Specified by:

getSubClassifiers in interface Classifier

Overrides:

getSubClassifiers in class AbstractClassifier

Returns:

an array of the classifiers of the ensemble

processChunk

protected void processChunk()

Processes a chunk of instances. This method is called after collecting a chunk of examples.

enforceMemoryLimit

protected void enforceMemoryLimit()

Checks if the memory limit is exceeded and if so prunes the classifiers in the ensemble.

computeMseR

protected double computeMseR()

Computes the MSEr threshold.

Returns:

The MSEr threshold.

computeMse

protected double computeMse(Classifier learner,
                weka.core.Instances chunk)

Computes the MSE of a learner for a given chunk of examples.

Parameters:

learner - classifier to compute error

chunk - chunk of examples

Returns:

the computed error.

getModelMeasurementsImpl

protected Measurement[] getModelMeasurementsImpl()

Adds ensemble weights to the measurements.

Specified by:

getModelMeasurementsImpl in class AbstractClassifier

Returns:

an array of measurements to be used in evaluation tasks

addToStored

protected Classifier addToStored(Classifier newClassifier,
                     double newClassifiersWeight)

Adds a classifier to the storage.

Parameters:

newClassifier - The classifier to add.

newClassifiersWeight - The new classifiers weight.