moa.classifiers.meta

Class ADACC

java.lang.Object

moa.AbstractMOAObject

moa.options.AbstractOptionHandler

moa.classifiers.AbstractClassifier

moa.classifiers.meta.DACC

moa.classifiers.meta.ADACC

All Implemented Interfaces:

Serializable, Classifier, AWTRenderable, MOAObject, OptionHandler

public class ADACC
extends DACC

Anticipative and Dynamic Adaptation to Concept Changes. Ensemble method for data streams that adapts to concept changes and deals with concept recurrence. Reference: JABER, G., CORNUEJOLS, A., and TARROUX, P. A New On-Line Learning Method for Coping with Recurring Concepts: The ADACC System. In : Neural Information Processing. Springer Berlin Heidelberg, 2013. p. 595-604.

Author:

Ghazal Jaber (ghazal.jaber@gmail.com)

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from class moa.classifiers.meta.DACC

DACC.Pair

Field Summary

Fields

Modifier and Type	Field and Description
protected int	addedPermanent Number of added snapshots
FloatOption	equivIndexSizeOption Threshold for concept equivalence
protected double	index Current stability index
protected static int	MAXPERMANENT Maximum number of snapshots (copies of classifiers kept in case of recurrence)
protected weka.core.Instances	recentChunk Last chunk of data of size (tau_size) to compute the stability index
FloatOption	stabIndexSizeOption Threshold for the stability index
protected int	tau_size Size of the evaluation window to compute the stability index
IntOption	tauSizeOption Evaluation window for the stability index computation
protected double	theta_diff Threshold values for the stability index and concept equivalence
protected double	theta_stab Threshold values for the stability index and concept equivalence

Fields inherited from class moa.classifiers.meta.DACC

combinationOption, ensemble, ensembleAges, ensembleWeights, ensembleWindows, evaluationSizeOption, learnerOption, maturityOption, memberCountOption, nbInstances

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
ADACC()

Method Summary

Methods

Modifier and Type	Method and Description
void	getModelDescription(StringBuilder out, int indent) Returns a string representation of the model.
protected Measurement[]	getModelMeasurementsImpl() 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.
protected int	getNbActiveClassifiers() Returns the number of classifiers used for prediction which includes the adaptive learners and the snapshots in ADACC
protected int	getNbAdaptiveClassifiers() Returns the number of adaptive classifiers in the ensemble which excludes the static snapshots in ADACC
String	getPurposeString() Gets the purpose of this object
protected void	initVariables() Initializes the method variables
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.meta.DACC

discardModel, getHalf, getMAXIndexes, getSubClassifiers, getVotesForInstance, getWVDIndexes, isRandomizable, resetLearningImpl, trainAndClassify, updateEvaluationWindow

Methods inherited from class moa.classifiers.AbstractClassifier

contextIsCompatible, copy, correctlyClassifies, getAttributeNameString, getAWTRenderer, getClassLabelString, getClassNameString, getDescription, getModelContext, getModelMeasurements, getNominalValueString, modelAttIndexToInstanceAttIndex, modelAttIndexToInstanceAttIndex, prepareForUseImpl, 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

tauSizeOption

public IntOption tauSizeOption

Evaluation window for the stability index computation

stabIndexSizeOption

public FloatOption stabIndexSizeOption

Threshold for the stability index

equivIndexSizeOption

public FloatOption equivIndexSizeOption

Threshold for concept equivalence

tau_size

protected int tau_size

Size of the evaluation window to compute the stability index

recentChunk

protected weka.core.Instances recentChunk

Last chunk of data of size (tau_size) to compute the stability index

theta_stab

protected double theta_stab

Threshold values for the stability index and concept equivalence

theta_diff

protected double theta_diff

Threshold values for the stability index and concept equivalence

index

protected double index

Current stability index

MAXPERMANENT

protected static final int MAXPERMANENT

Maximum number of snapshots (copies of classifiers kept in case of recurrence)

See Also:

Constant Field Values

addedPermanent

protected int addedPermanent

Number of added snapshots

Constructor Detail

ADACC

public ADACC()

Method Detail

getPurposeString

public String getPurposeString()

Description copied from interface: OptionHandler

Gets the purpose of this object

Specified by:

getPurposeString in interface OptionHandler

Overrides:

getPurposeString in class DACC

Returns:

the string with the purpose of this object

initVariables

protected void initVariables()

Description copied from class: DACC

Initializes the method variables

Overrides:

initVariables in class DACC

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.

Overrides:

trainOnInstanceImpl in class DACC

Parameters:

inst - the instance to be used for training

getModelDescription

public void getModelDescription(StringBuilder out,
                       int indent)

Description copied from class: AbstractClassifier

Returns a string representation of the model.

Overrides:

getModelDescription in class DACC

Parameters:

out - the stringbuilder to add the description

indent - the number of characters to indent

getModelMeasurementsImpl

protected Measurement[] getModelMeasurementsImpl()

Description copied from class: AbstractClassifier

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.

Overrides:

getModelMeasurementsImpl in class DACC

Returns:

an array of measurements to be used in evaluation tasks

getNbActiveClassifiers

protected int getNbActiveClassifiers()

Description copied from class: DACC

Returns the number of classifiers used for prediction which includes the adaptive learners and the snapshots in ADACC

Overrides:

getNbActiveClassifiers in class DACC

Returns:

the number of classifiers used for prediction

getNbAdaptiveClassifiers

protected int getNbAdaptiveClassifiers()

Description copied from class: DACC

Returns the number of adaptive classifiers in the ensemble which excludes the static snapshots in ADACC

Overrides:

getNbAdaptiveClassifiers in class DACC

Returns:

the number of adaptive classifiers