Package moa.classifiers.trees
Class ARFHoeffdingTree
- 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.trees.HoeffdingTree
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- moa.classifiers.trees.ARFHoeffdingTree
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- All Implemented Interfaces:
Configurable
,Serializable
,CapabilitiesHandler
,Classifier
,MultiClassClassifier
,AWTRenderable
,Learner<Example<Instance>>
,MOAObject
,OptionHandler
public class ARFHoeffdingTree extends HoeffdingTree
Adaptive Random Forest Hoeffding Tree.Adaptive Random Forest Hoeffding Tree. This is the base model for the Adaptive Random Forest ensemble learner (See moa.classifiers.meta.AdaptiveRandomForest.java). This Hoeffding Tree includes a subspace size k parameter, which defines the number of randomly selected features to be considered at each split.
See details in:
Heitor Murilo Gomes, Albert Bifet, Jesse Read, Jean Paul Barddal, Fabricio Enembreck, Bernhard Pfharinger, Geoff Holmes, Talel Abdessalem. Adaptive random forests for evolving data stream classification. In Machine Learning, DOI: 10.1007/s10994-017-5642-8, Springer, 2017.- Version:
- $Revision: 1 $
- Author:
- Heitor Murilo Gomes (heitor_murilo_gomes at yahoo dot com dot br)
- See Also:
- Serialized Form
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Nested Class Summary
Nested Classes Modifier and Type Class Description static class
ARFHoeffdingTree.LearningNodeNB
static class
ARFHoeffdingTree.LearningNodeNBAdaptive
static class
ARFHoeffdingTree.RandomLearningNode
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Nested classes/interfaces inherited from class moa.classifiers.trees.HoeffdingTree
HoeffdingTree.ActiveLearningNode, HoeffdingTree.FoundNode, HoeffdingTree.InactiveLearningNode, HoeffdingTree.LearningNode, HoeffdingTree.Node, HoeffdingTree.SplitNode
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Field Summary
Fields Modifier and Type Field Description IntOption
subspaceSizeOption
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Fields inherited from class moa.classifiers.trees.HoeffdingTree
activeLeafByteSizeEstimate, activeLeafNodeCount, binarySplitsOption, byteSizeEstimateOverheadFraction, decisionNodeCount, gracePeriodOption, growthAllowed, inactiveLeafByteSizeEstimate, inactiveLeafNodeCount, leafpredictionOption, maxByteSizeOption, memoryEstimatePeriodOption, nbThresholdOption, nominalEstimatorOption, noPrePruneOption, numericEstimatorOption, removePoorAttsOption, splitConfidenceOption, splitCriterionOption, stopMemManagementOption, tieThresholdOption, treeRoot
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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 ARFHoeffdingTree()
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description String
getPurposeString()
Dictionary with option texts and objectsboolean
isRandomizable()
Gets whether this learner needs a random seed.protected HoeffdingTree.LearningNode
newLearningNode(double[] initialClassObservations)
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Methods inherited from class moa.classifiers.trees.HoeffdingTree
activateLearningNode, attemptToSplit, calcByteSize, computeHoeffdingBound, deactivateAllLeaves, deactivateLearningNode, defineImmutableCapabilities, enforceTrackerLimit, estimateModelByteSizes, findLearningNodes, findLearningNodes, getModelDescription, getModelMeasurementsImpl, getNodeCount, getTreeRoot, getVotesForInstance, measureByteSize, measureTreeDepth, newLearningNode, newNominalClassObserver, newNumericClassObserver, newSplitNode, newSplitNode, resetLearningImpl, trainOnInstanceImpl
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Methods inherited from class moa.classifiers.AbstractClassifier
contextIsCompatible, copy, correctlyClassifies, getAttributeNameString, getAWTRenderer, getClassLabelString, getClassNameString, getDescription, getModel, getModelContext, getModelMeasurements, getNominalValueString, getPredictionForInstance, getPredictionForInstance, getSubClassifiers, getSublearners, getVotesForInstance, modelAttIndexToInstanceAttIndex, modelAttIndexToInstanceAttIndex, prepareForUseImpl, resetLearning, setModelContext, 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, 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.options.OptionHandler
getCLICreationString, getOptions, prepareForUse, prepareForUse
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Field Detail
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subspaceSizeOption
public IntOption subspaceSizeOption
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Method Detail
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getPurposeString
public String getPurposeString()
Description copied from class:AbstractOptionHandler
Dictionary with option texts and objects- Specified by:
getPurposeString
in interfaceOptionHandler
- Overrides:
getPurposeString
in classHoeffdingTree
- Returns:
- the string with the purpose of this object
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newLearningNode
protected HoeffdingTree.LearningNode newLearningNode(double[] initialClassObservations)
- Overrides:
newLearningNode
in classHoeffdingTree
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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 interfaceLearner<Example<Instance>>
- Overrides:
isRandomizable
in classHoeffdingTree
- Returns:
- true if the learner needs a random seed.
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