Package moa.classifiers.meta
Class HeterogeneousEnsembleBlastFadingFactors
- java.lang.Object
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- moa.AbstractMOAObject
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- moa.options.AbstractOptionHandler
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- moa.classifiers.AbstractClassifier
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- moa.classifiers.meta.HeterogeneousEnsembleAbstract
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- moa.classifiers.meta.HeterogeneousEnsembleBlastFadingFactors
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- All Implemented Interfaces:
Configurable
,Serializable
,CapabilitiesHandler
,Classifier
,MultiClassClassifier
,AWTRenderable
,Learner<Example<Instance>>
,MOAObject
,OptionHandler
public class HeterogeneousEnsembleBlastFadingFactors extends HeterogeneousEnsembleAbstract implements MultiClassClassifier
BLAST (Best Last) for Heterogeneous Ensembles implemented with Fading FactorsGiven a set of (heterogeneous) classifiers, BLAST builds an ensemble, and determines the weights of all ensemble members based on their performance on recent observed instances. This implementation uses fading factors, to emphasize the importance of recent predictions and fade away old predictions.
J. N. van Rijn, G. Holmes, B. Pfahringer, J. Vanschoren. Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams. In 2015 IEEE International Conference on Data Mining, pages 1003-1008. IEEE, 2015.
Parameters:
- -f : Fading factor
- -b : Comma-separated string of classifiers
- -g : Grace period (1 = optimal)
- -k : Number of active classifiers
- Version:
- $Revision: 1 $
- Author:
- Jan N. van Rijn (j.n.van.rijn@liacs.leidenuniv.nl)
- See Also:
- Serialized Form
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Field Summary
Fields Modifier and Type Field Description FloatOption
alphaOption
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Fields inherited from class moa.classifiers.meta.HeterogeneousEnsembleAbstract
activeClassifiersOption, baselearnersOption, ensemble, gracePerionOption, historyTotal, instancesSeen, weightClassifiersOption
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Fields inherited from class moa.classifiers.AbstractClassifier
classifierRandom, modelContext, randomSeed, randomSeedOption, trainingWeightSeenByModel
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Fields inherited from class moa.options.AbstractOptionHandler
config
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Constructor Summary
Constructors Constructor Description HeterogeneousEnsembleBlastFadingFactors()
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description 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.meta.HeterogeneousEnsembleAbstract
getEnsembleSize, getMemberCliString, getModelDescription, getModelMeasurementsImpl, getPurposeString, getVotesForInstance, isRandomizable, maxIndex, normalize, prepareForUseImpl, setModelContext, topK
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Methods inherited from class moa.classifiers.AbstractClassifier
contextIsCompatible, copy, correctlyClassifies, defineImmutableCapabilities, getAttributeNameString, getAWTRenderer, getClassLabelString, getClassNameString, getDescription, getModel, getModelContext, getModelMeasurements, getNominalValueString, getPredictionForInstance, getPredictionForInstance, getSubClassifiers, getSublearners, getVotesForInstance, modelAttIndexToInstanceAttIndex, modelAttIndexToInstanceAttIndex, resetLearning, setRandomSeed, trainingHasStarted, trainingWeightSeenByModel, trainOnInstance, trainOnInstance
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Methods inherited from class moa.options.AbstractOptionHandler
getCLICreationString, getOptions, getPreparedClassOption, prepareClassOptions, prepareForUse, prepareForUse
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Methods inherited from class moa.AbstractMOAObject
copy, measureByteSize, measureByteSize, toString
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Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
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Methods inherited from interface moa.capabilities.CapabilitiesHandler
getCapabilities
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Methods inherited from interface moa.MOAObject
measureByteSize
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Methods inherited from interface moa.options.OptionHandler
getCLICreationString, getOptions, prepareForUse, prepareForUse
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Field Detail
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alphaOption
public FloatOption alphaOption
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Method Detail
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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 classAbstractClassifier
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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 classAbstractClassifier
- Parameters:
inst
- the instance to be used for training
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