Package moa.classifiers.meta

Class ADOB

java.lang.Object

moa.AbstractMOAObject

moa.options.AbstractOptionHandler

moa.classifiers.AbstractClassifier

moa.classifiers.meta.ADOB

All Implemented Interfaces:

Configurable, java.io.Serializable, CapabilitiesHandler, Classifier, MultiClassClassifier, AWTRenderable, Learner<Example<Instance>>, MOAObject, OptionHandler

public class ADOB
extends AbstractClassifier
implements MultiClassClassifier

Adaptable Diversity-based Online Boosting (ADOB) is a modified version of the online boosting, as proposed by Oza and Russell, which is aimed at speeding up the experts recovery after concept drifts. published as: Silas G. T. C. Santos, Paulo M. Goncalves Jr., Geyson D. S. Silva, and Roberto S. M. Barros: Speeding Up Recovery from Concept Drifts. In book: Machine Learning and Knowledge Discovery in Databases, ECML/PKDD 2014, Part III, LNCS 8726, pp. 179-194. 09/2014. DOI: 10.1007/978-3-662-44845-8_12

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field	Description
ClassOption	baseLearnerOption
protected Classifier[]	ensemble
IntOption	ensembleSizeOption
protected int[]	orderPosition
FlagOption	pureBoostOption
protected double[]	scms
protected double[]	swms

Fields inherited from class moa.classifiers.AbstractClassifier

classifierRandom, modelContext, randomSeed, randomSeedOption, trainingWeightSeenByModel

Fields inherited from class moa.options.AbstractOptionHandler

config

Constructor Summary

Constructors

Constructor	Description
ADOB()

Method Summary

Modifier and Type	Method	Description
protected double	getEnsembleMemberWeight(int i)
void	getModelDescription(java.lang.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.
java.lang.String	getPurposeString()	Dictionary with option texts and objects
Classifier[]	getSubClassifiers()	Gets the classifiers of this ensemble.
double[]	getVotesForInstance(Instance inst)	Predicts the class memberships for a given instance.
boolean	isRandomizable()	Gets whether this learner needs a random seed.
void	resetLearningImpl()	Resets this classifier.
void	trainOnInstanceImpl(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, defineImmutableCapabilities, getAttributeNameString, getAWTRenderer, getClassLabelString, getClassNameString, getDescription, getModel, getModelContext, getModelMeasurements, getNominalValueString, getPredictionForInstance, getPredictionForInstance, getSublearners, getVotesForInstance, modelAttIndexToInstanceAttIndex, modelAttIndexToInstanceAttIndex, prepareForUseImpl, resetLearning, setModelContext, setRandomSeed, trainingHasStarted, trainingWeightSeenByModel, trainOnInstance, trainOnInstance

Methods inherited from class moa.AbstractMOAObject

copy, measureByteSize, measureByteSize, toString

Methods inherited from class moa.options.AbstractOptionHandler

getCLICreationString, getOptions, getPreparedClassOption, prepareClassOptions, prepareForUse, prepareForUse

Methods inherited from interface moa.capabilities.CapabilitiesHandler

getCapabilities

Methods inherited from interface moa.MOAObject

measureByteSize

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

Field Detail

baseLearnerOption

public ClassOption baseLearnerOption

ensembleSizeOption

public IntOption ensembleSizeOption

pureBoostOption

public FlagOption pureBoostOption

ensemble

protected Classifier[] ensemble

orderPosition

protected int[] orderPosition

scms

protected double[] scms

swms

protected double[] swms

Constructor Detail

ADOB

public ADOB()

Method Detail

getPurposeString

public java.lang.String getPurposeString()

Description copied from class: AbstractOptionHandler

Dictionary with option texts and objects

Specified by:

getPurposeString in interface OptionHandler

Overrides:

getPurposeString in class AbstractClassifier

Returns:

the string with the purpose of this object

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(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

getEnsembleMemberWeight

protected double getEnsembleMemberWeight(int i)

getVotesForInstance

public double[] getVotesForInstance(Instance inst)

Description copied from interface: Classifier

Predicts the class memberships for a given instance. If an instance is unclassified, the returned array elements must be all zero.

Specified by:

getVotesForInstance in interface Classifier

Specified by:

getVotesForInstance in class AbstractClassifier

Parameters:

inst - the instance to be classified

Returns:

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

isRandomizable

public boolean isRandomizable()

Description copied from interface: Learner

Gets whether this learner needs a random seed. Examples of methods that needs a random seed are bagging and boosting.

Specified by:

isRandomizable in interface Learner<Example<Instance>>

Returns:

true if the learner needs a random seed.

getModelDescription

public void getModelDescription(java.lang.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

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.

Specified by:

getModelMeasurementsImpl in class AbstractClassifier

Returns:

an array of measurements to be used in evaluation tasks

getSubClassifiers

public Classifier[] getSubClassifiers()

Description copied from interface: Classifier

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

Specified by:

getSubClassifiers in interface Classifier

Overrides:

getSubClassifiers in class AbstractClassifier

Returns:

an array of the learners of the ensemble