Package moa.classifiers.meta
Class PairedLearners
- 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.PairedLearners
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- All Implemented Interfaces:
Configurable
,Serializable
,CapabilitiesHandler
,Classifier
,MultiClassClassifier
,AWTRenderable
,Learner<Example<Instance>>
,MOAObject
,OptionHandler
public class PairedLearners extends AbstractClassifier implements MultiClassClassifier
Creates two classifiers: a stable and a reactive. The first represents the actual stable concept, while the second is trained on the most recent data. If the accuracy of the reactive is higher than that of the stable, it means the concept has changed. The stable classifier is then substituted by the reactive, and the reactive is reset.Stephen H. Bach, Marcus A. Maloof, "Paired Learners for Concept Drift", Eighth IEEE International Conference on Data Mining (ICDM), 2008, pp.23-32
- Author:
- Paulo Gonçalves (paulogoncalves at recife.ifpe.edu.br)
- See Also:
- Serialized Form
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Field Summary
Fields Modifier and Type Field Description protected int[]
c
protected int
changeDetected
protected int
i
protected Instance[]
instances
protected int
numberOfErrors
protected Classifier
reactiveLearner
ClassOption
reactiveLearnerOption
protected Classifier
stableLearner
ClassOption
stableLearnerOption
protected int
t
protected int
theta
FloatOption
thresholdOption
protected int
w
IntOption
windowSizeOption
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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 PairedLearners()
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description void
getModelDescription(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.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, getPurposeString, 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, 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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stableLearnerOption
public ClassOption stableLearnerOption
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reactiveLearnerOption
public ClassOption reactiveLearnerOption
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windowSizeOption
public IntOption windowSizeOption
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thresholdOption
public FloatOption thresholdOption
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c
protected int[] c
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stableLearner
protected Classifier stableLearner
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reactiveLearner
protected Classifier reactiveLearner
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t
protected int t
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w
protected int w
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theta
protected int theta
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instances
protected Instance[] instances
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changeDetected
protected int changeDetected
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numberOfErrors
protected int numberOfErrors
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i
protected int i
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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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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 interfaceClassifier
- Specified by:
getVotesForInstance
in classAbstractClassifier
- Parameters:
inst
- the instance to be classified- Returns:
- an array containing the estimated membership probabilities of the test instance in each class
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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>>
- Returns:
- true if the learner needs a random seed.
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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 classAbstractClassifier
- Returns:
- an array of measurements to be used in evaluation tasks
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getModelDescription
public void getModelDescription(StringBuilder out, int indent)
Description copied from class:AbstractClassifier
Returns a string representation of the model.- Specified by:
getModelDescription
in classAbstractClassifier
- Parameters:
out
- the stringbuilder to add the descriptionindent
- the number of characters to indent
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