Package 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:
Configurable
,Serializable
,CapabilitiesHandler
,Classifier
,MultiClassClassifier
,AWTRenderable
,Learner<Example<Instance>>
,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 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 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
config
-
-
Constructor Summary
Constructors Constructor Description LimAttHoeffdingTree()
-
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)
void
setlistAttributes(int[] list)
-
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
-
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
-
Methods inherited from class moa.options.AbstractOptionHandler
getCLICreationString, getOptions, 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.capabilities.CapabilitiesHandler
getCapabilities
-
Methods inherited from interface moa.options.OptionHandler
getCLICreationString, getOptions, prepareForUse, prepareForUse
-
-
-
-
Method Detail
-
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
-
setlistAttributes
public void setlistAttributes(int[] list)
-
newLearningNode
protected HoeffdingTree.LearningNode newLearningNode(double[] initialClassObservations)
- Overrides:
newLearningNode
in classHoeffdingTree
-
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.
-
-