moa.classifiers.rules

Class RuleClassifierNBayes

java.lang.Object

moa.AbstractMOAObject

moa.options.AbstractOptionHandler

moa.classifiers.AbstractClassifier

moa.classifiers.rules.RuleClassifier

moa.classifiers.rules.RuleClassifierNBayes

All Implemented Interfaces:

Serializable, Classifier, AWTRenderable, MOAObject, OptionHandler

public class RuleClassifierNBayes
extends RuleClassifier

This classifier learn ordered and unordered rule set from data stream with naive Bayes learners.

This algorithm also does the detection of anomalies.

Learning Decision RuleClassifications from Data Streams, IJCAI 2011, J. Gama, P. Kosina

Parameters:

• -q: The number of instances a leaf should observe before permitting Naive Bayes.
• -p: Minimum value of p
• -t: Tie Threshold
• -c: Split Confidence
• -g: GracePeriod, the number of instances a leaf should observe between split attempts
• -o: Prediction function to use. Ex:FirstHit
• -r: Learn ordered or unordered rule

Version:

$Revision: 2 $

Author:

P. Kosina, E. Almeida, J. Gama

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
IntOption	nbThresholdOption

Fields inherited from class moa.classifiers.rules.RuleClassifier

anomalyDetectionOption, anomalyNumInstThresholdOption, anomalyProbabilityThresholdOption, attributeObservers, attributeObserversGauss, caseAnomaly, caseAnomalySupervised, gracePeriodOption, instance, observedClassDistribution, orderedRulesOption, PminOption, predictionFunctionOption, probabilityThresholdOption, ruleAnomaliesIndex, ruleAnomaliesIndexSupervised, ruleAttribAnomalyStatistics, ruleAttribAnomalyStatisticsSupervised, ruleClassIndex, ruleSet, ruleSetAnomalies, ruleSetAnomaliesSupervised, saveBestEntropy, saveBestEntropyNominalAttrib, saveBestGlobalEntropy, saveBestValGlobalEntropy, saveTheBest, splitConfidenceOption, Supervised, tieThresholdOption, Unsupervised

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
RuleClassifierNBayes()

Method Summary

Methods

Modifier and Type	Method and Description
protected double[]	exponential(double[] votes)
protected double[]	firstHitNB(weka.core.Instance inst)
double[]	getVotesForInstance(weka.core.Instance inst) Predicts the class memberships for a given instance.
protected double[]	normalize(double[] votes)
protected double[]	weightedMaxNB(weka.core.Instance inst)
protected double[]	weightedSumNB(weka.core.Instance inst)

Methods inherited from class moa.classifiers.rules.RuleClassifier

checkBestAttrib, computeAnomalySupervised, computeAnomalyUnsupervised, ComputeHoeffdingBound, computeMean, computeProbability, computeSD, createRule, entropy, expandeRule, findBestValEntropy, findBestValEntropyNominalAtt, firstHit, getBestSecondBestEntropy, getCountNominalAttrib, getModelDescription, getModelDescriptionNoAnomalyDetection, getModelMeasurementsImpl, getPurposeString, getRuleMajorityClassIndex, getWeightSeen, initializeRuleStatistics, isRandomizable, mainFindBestValEntropy, manageMemory, newNominalClassObserver, newNumericClassObserver, newNumericClassObserver2, oberversDistribProb, printAnomaliesSupervised, printAnomaliesUnsupervised, resetLearningImpl, round, theBestAttributes, trainOnInstanceImpl, updateRuleAttribStatistics, weightedMax, weightedSum

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, 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

nbThresholdOption

public IntOption nbThresholdOption

Constructor Detail

RuleClassifierNBayes

public RuleClassifierNBayes()

Method Detail

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.

Specified by:

getVotesForInstance in interface Classifier

Overrides:

getVotesForInstance in class RuleClassifier

Parameters:

inst - the instance to be classified

Returns:

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

firstHitNB

protected double[] firstHitNB(weka.core.Instance inst)

weightedMaxNB

protected double[] weightedMaxNB(weka.core.Instance inst)

weightedSumNB

protected double[] weightedSumNB(weka.core.Instance inst)

normalize

protected double[] normalize(double[] votes)

exponential

protected double[] exponential(double[] votes)