moa.classifiers.meta

Class LimAttClassifier

java.lang.Object

moa.AbstractMOAObject

moa.options.AbstractOptionHandler

moa.classifiers.AbstractClassifier

moa.classifiers.meta.LimAttClassifier

All Implemented Interfaces:

Serializable, Classifier, AWTRenderable, MOAObject, OptionHandler

public class LimAttClassifier
extends AbstractClassifier

Ensemble Combining Restricted Hoeffding Trees using Stacking. It produces a classification model based on an ensemble of restricted decision trees, where each tree is built from a distinct subset of the attributes. The overall model is formed by combining the log-odds of the predicted class probabilities of these trees using sigmoid perceptrons, with one perceptron per class. In contrast to the standard boosting approach, which forms an ensemble classifier in a greedy fashion, building each tree in sequence and assigning corresponding weights as a by-product, our method generates each tree in parallel and combines them using perceptron classifiers by adopting the stacking approach. 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), Eibe Frank (eibe{[at]}cs{[dot]}waikato{[dot]}ac{[dot]}nz)

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
class	LimAttClassifier.CombinationGenerator

Field Summary

Fields

Modifier and Type	Field and Description
protected ADWIN[]	ADError
FlagOption	adwinReplaceWorstClassifierOption
ClassOption	baseLearnerOption
FlagOption	bigTreesOption
FloatOption	deltaAdwinOption
protected Classifier[]	ensemble
protected boolean	initClassifiers
IntOption	initialNumInstancesOption
protected boolean	initMatrixCodes
FloatOption	learningRatioOption
protected int[][]	matrixCodes
IntOption	numAttributesOption
protected int	numberAttributes
protected int	numberOfChangesDetected
IntOption	numEnsemblePruningOption
protected int	numInstances
FloatOption	oddsOffsetOption
FloatOption	penaltyFactorOption
FlagOption	pruneOption
protected boolean	reset
protected double[][]	weightAttribute
FloatOption	weightShrinkOption

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
LimAttClassifier()

Method Summary

Methods

Modifier and Type	Method and 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.
String	getPurposeString() Gets the purpose of this object
Classifier[]	getSubClassifiers() Gets the classifiers of this ensemble.
double[]	getVotesForInstance(weka.core.Instance inst) Predicts the class memberships for a given instance.
double[]	getVotesForInstancePerceptron(double[][] votesEnsemble, int[] bestClassifiers, int numClasses)
boolean	isRandomizable() Gets whether this classifier needs a random seed.
double	prediction(double[][] votes, int classVal)
double	predictionPruning(double[][] votes, int[] bestClassifiers, int classVal)
void	resetLearningImpl() Resets this classifier.
void	trainOnInstanceImpl(weka.core.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.
void	trainOnInstanceImplPerceptron(int numClasses, int actualClass, double[][] votes)

Methods inherited from class moa.classifiers.AbstractClassifier

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

Methods inherited from interface moa.MOAObject

measureByteSize

Field Detail

baseLearnerOption

public ClassOption baseLearnerOption

numAttributesOption

public IntOption numAttributesOption

weightShrinkOption

public FloatOption weightShrinkOption

deltaAdwinOption

public FloatOption deltaAdwinOption

oddsOffsetOption

public FloatOption oddsOffsetOption

pruneOption

public FlagOption pruneOption

bigTreesOption

public FlagOption bigTreesOption

numEnsemblePruningOption

public IntOption numEnsemblePruningOption

adwinReplaceWorstClassifierOption

public FlagOption adwinReplaceWorstClassifierOption

ensemble

protected Classifier[] ensemble

ADError

protected ADWIN[] ADError

numberOfChangesDetected

protected int numberOfChangesDetected

matrixCodes

protected int[][] matrixCodes

initMatrixCodes

protected boolean initMatrixCodes

initClassifiers

protected boolean initClassifiers

numberAttributes

protected int numberAttributes

numInstances

protected int numInstances

learningRatioOption

public FloatOption learningRatioOption

penaltyFactorOption

public FloatOption penaltyFactorOption

initialNumInstancesOption

public IntOption initialNumInstancesOption

weightAttribute

protected double[][] weightAttribute

reset

protected boolean reset

Constructor Detail

LimAttClassifier

public LimAttClassifier()

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 AbstractClassifier

Returns:

the string with the purpose of this object

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 class AbstractClassifier

trainOnInstanceImpl

public void trainOnInstanceImpl(weka.core.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 class AbstractClassifier

Parameters:

inst - the instance to be used for training

getVotesForInstance

public double[] getVotesForInstance(weka.core.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.

Parameters:

inst - the instance to be classified

Returns:

an array containing the estimated membership probabilities of the test instance in each class

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.

Returns:

true if the classifier needs a random seed.

getModelDescription

public void getModelDescription(StringBuilder out,
                       int indent)

Description copied from class: AbstractClassifier

Returns a string representation of the model.

Specified by:

getModelDescription in class AbstractClassifier

Parameters:

out - the stringbuilder to add the description

indent - the number of characters to indent

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 class AbstractClassifier

Returns:

an array of measurements to be used in evaluation tasks

getSubClassifiers

public Classifier[] getSubClassifiers()

Description copied from interface: Classifier

Gets the classifiers of this ensemble. Returns null if this classifier is a single classifier.

Specified by:

getSubClassifiers in interface Classifier

Overrides:

getSubClassifiers in class AbstractClassifier

Returns:

an array of the classifiers of the ensemble

trainOnInstanceImplPerceptron

public void trainOnInstanceImplPerceptron(int numClasses,
                                 int actualClass,
                                 double[][] votes)

predictionPruning

public double predictionPruning(double[][] votes,
                       int[] bestClassifiers,
                       int classVal)

prediction

public double prediction(double[][] votes,
                int classVal)

getVotesForInstancePerceptron

public double[] getVotesForInstancePerceptron(double[][] votesEnsemble,
                                     int[] bestClassifiers,
                                     int numClasses)