moa.classifiers.trees

Class LimAttHoeffdingTree

java.lang.Object

moa.AbstractMOAObject

moa.options.AbstractOptionHandler

moa.classifiers.AbstractClassifier

moa.classifiers.trees.HoeffdingTree

moa.classifiers.trees.LimAttHoeffdingTree

All Implemented Interfaces:

Serializable, Classifier, AWTRenderable, MOAObject, OptionHandler

public class LimAttHoeffdingTree
extends HoeffdingTree

Hoeffding decision trees with a restricted number of attributes for data streams. LimAttClassifier is the stacking method that can be used with these decision trees. For more information see,

Albert Bifet, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer: Accurate Ensembles for Data Streams: Combining Restricted Hoeffding Trees using Stacking. Journal of Machine Learning Research - Proceedings Track 13: 225-240 (2010) * BibTeX:

 @article{BifetFHP10,
 author    = {Albert Bifet and
              Eibe Frank and
              Geoffrey Holmes and
              Bernhard Pfahringer},
 title     = {Accurate Ensembles for Data Streams: Combining Restricted
              Hoeffding Trees using Stacking},
 journal   = {Journal of Machine Learning Research - Proceedings Track},
 volume    = {13},
 year      = {2010},
 pages     = {225-240}
 }
 

Version:

$Revision: 7 $

Author:

Albert Bifet (abifet at cs dot waikato dot ac dot nz)

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	LimAttHoeffdingTree.LearningNodeNB
static class	LimAttHoeffdingTree.LearningNodeNBAdaptive
static class	LimAttHoeffdingTree.LimAttLearningNode

Nested classes/interfaces inherited from class moa.classifiers.trees.HoeffdingTree

HoeffdingTree.ActiveLearningNode, HoeffdingTree.FoundNode, HoeffdingTree.InactiveLearningNode, HoeffdingTree.LearningNode, HoeffdingTree.Node, HoeffdingTree.SplitNode

Field Summary

Fields

Modifier and Type	Field and Description
protected int[]	listAttributes

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

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
LimAttHoeffdingTree()

Method Summary

Methods

Modifier and Type	Method and Description
String	getPurposeString() Gets the purpose of this object
boolean	isRandomizable() Gets whether this classifier needs a random seed.
protected HoeffdingTree.LearningNode	newLearningNode(double[] initialClassObservations)
void	setlistAttributes(int[] list)

Methods inherited from class moa.classifiers.trees.HoeffdingTree

activateLearningNode, attemptToSplit, calcByteSize, computeHoeffdingBound, deactivateAllLeaves, deactivateLearningNode, enforceTrackerLimit, estimateModelByteSizes, findLearningNodes, findLearningNodes, getModelDescription, getModelMeasurementsImpl, getVotesForInstance, measureByteSize, measureTreeDepth, newLearningNode, newNominalClassObserver, newNumericClassObserver, newSplitNode, newSplitNode, resetLearningImpl, trainOnInstanceImpl

Methods inherited from class moa.classifiers.AbstractClassifier

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

Field Detail

listAttributes

protected int[] listAttributes

Constructor Detail

LimAttHoeffdingTree

public LimAttHoeffdingTree()

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 HoeffdingTree

Returns:

the string with the purpose of this object

setlistAttributes

public void setlistAttributes(int[] list)

newLearningNode

protected HoeffdingTree.LearningNode newLearningNode(double[] initialClassObservations)

Overrides:

newLearningNode in class HoeffdingTree

isRandomizable

public boolean isRandomizable()

Description copied from interface: Classifier

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

Specified by:

isRandomizable in interface Classifier

Overrides:

isRandomizable in class HoeffdingTree

Returns:

true if the classifier needs a random seed.