Uses of Class
moa.AbstractMOAObject
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Uses of AbstractMOAObject in moa.classifiers
Subclasses of AbstractMOAObject in moa.classifiers Modifier and Type Class Description class
AbstractClassifier
class
AbstractMultiLabelLearner
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Uses of AbstractMOAObject in moa.classifiers.active
Subclasses of AbstractMOAObject in moa.classifiers.active Modifier and Type Class Description class
ALRandom
class
ALUncertainty
Active learning setting for evolving data streams. -
Uses of AbstractMOAObject in moa.classifiers.active.budget
Subclasses of AbstractMOAObject in moa.classifiers.active.budget Modifier and Type Class Description class
FixedBM
-
Uses of AbstractMOAObject in moa.classifiers.bayes
Subclasses of AbstractMOAObject in moa.classifiers.bayes Modifier and Type Class Description class
NaiveBayes
Naive Bayes incremental learner.class
NaiveBayesMultinomial
Class for building and using a multinomial Naive Bayes classifier. -
Uses of AbstractMOAObject in moa.classifiers.core
Subclasses of AbstractMOAObject in moa.classifiers.core Modifier and Type Class Description class
AttributeSplitSuggestion
Class for computing attribute split suggestions given a split test. -
Uses of AbstractMOAObject in moa.classifiers.core.attributeclassobservers
Subclasses of AbstractMOAObject in moa.classifiers.core.attributeclassobservers Modifier and Type Class Description class
BinaryTreeNumericAttributeClassObserver
Class for observing the class data distribution for a numeric attribute using a binary tree.class
BinaryTreeNumericAttributeClassObserverRegression
Class for observing the class data distribution for a numeric attribute using a binary tree.class
FIMTDDNumericAttributeClassObserver
class
GaussianNumericAttributeClassObserver
Class for observing the class data distribution for a numeric attribute using gaussian estimators.class
GreenwaldKhannaNumericAttributeClassObserver
Class for observing the class data distribution for a numeric attribute using Greenwald and Khanna methodology.class
NominalAttributeClassObserver
Class for observing the class data distribution for a nominal attribute.class
NullAttributeClassObserver
Class for observing the class data distribution for a null attribute.class
VFMLNumericAttributeClassObserver
Class for observing the class data distribution for a numeric attribute as in VFML. -
Uses of AbstractMOAObject in moa.classifiers.core.conditionaltests
Subclasses of AbstractMOAObject in moa.classifiers.core.conditionaltests Modifier and Type Class Description class
InstanceConditionalBinaryTest
Abstract binary conditional test for instances to use to split nodes in Hoeffding trees.class
InstanceConditionalTest
Abstract conditional test for instances to use to split nodes in Hoeffding trees.class
NominalAttributeBinaryTest
Nominal binary conditional test for instances to use to split nodes in Hoeffding trees.class
NominalAttributeMultiwayTest
Nominal multi way conditional test for instances to use to split nodes in Hoeffding trees.class
NumericAttributeBinaryTest
Numeric binary conditional test for instances to use to split nodes in Hoeffding trees. -
Uses of AbstractMOAObject in moa.classifiers.core.driftdetection
Subclasses of AbstractMOAObject in moa.classifiers.core.driftdetection Modifier and Type Class Description class
AbstractChangeDetector
Abstract Change Detector.class
ADWIN
ADaptive sliding WINdow method.class
ADWINChangeDetector
Drift detection method based in ADWIN.class
CusumDM
Drift detection method based in Cusumclass
DDM
Drift detection method based in DDM method of Joao Gama SBIA 2004.class
EDDM
Drift detection method based in EDDM method of Manuel Baena et al.class
EnsembleDriftDetectionMethods
Ensemble Drift detection methodclass
EWMAChartDM
Drift detection method based in EWMA Charts of Ross, Adams, Tasoulis and Hand 2012class
GeometricMovingAverageDM
Drift detection method based in Geometric Moving Average Testclass
HDDM_A_Test
Online drift detection method based on Hoeffding's bounds.class
HDDM_W_Test
Online drift detection method based on McDiarmid's bounds.class
PageHinkleyDM
Drift detection method based in Page Hinkley Test.class
RDDM
class
SEEDChangeDetector
Drift detection method as published in:class
SeqDrift1ChangeDetector
SeqDrift1ChangeDetector.java.class
SeqDrift1ChangeDetector.SeqDrift1
SeqDrift1 uses sliding window to build a sequential change detection model that uses statistically sound guarantees defined using Bernstein Bound on false positive and false negative rates.class
SeqDrift2ChangeDetector
SeqDriftChangeDetector.java.class
SeqDrift2ChangeDetector.SeqDrift2
SeqDrift2 uses reservoir sampling to build a sequential change detection model that uses statistically sound guarantees defined using Bernstein Bound on false positive and false negative rates.class
STEPD
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Uses of AbstractMOAObject in moa.classifiers.core.splitcriteria
Subclasses of AbstractMOAObject in moa.classifiers.core.splitcriteria Modifier and Type Class Description class
GiniSplitCriterion
Class for computing splitting criteria using Gini with respect to distributions of class values.class
InfoGainSplitCriterion
Class for computing splitting criteria using information gain with respect to distributions of class values.class
InfoGainSplitCriterionMultilabel
Class for computing splitting criteria using information gain with respect to distributions of class values for Multilabel data.class
SDRSplitCriterion
class
VarianceReductionSplitCriterion
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Uses of AbstractMOAObject in moa.classifiers.core.statisticaltests
Subclasses of AbstractMOAObject in moa.classifiers.core.statisticaltests Modifier and Type Class Description class
Cramer
Implements the Multivariate Non-parametric Cramer Von Mises Statistical Test.class
KNN
Implements the multivariate non-parametric KNN statistical test. -
Uses of AbstractMOAObject in moa.classifiers.deeplearning
Subclasses of AbstractMOAObject in moa.classifiers.deeplearning Modifier and Type Class Description class
CAND
Continuously Adaptive Neural networks for Data streamsclass
MLP
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Uses of AbstractMOAObject in moa.classifiers.drift
Subclasses of AbstractMOAObject in moa.classifiers.drift Modifier and Type Class Description class
DriftDetectionMethodClassifier
Class for handling concept drift datasets with a wrapper on a classifier.class
SingleClassifierDrift
Class for handling concept drift datasets with a wrapper on a classifier. -
Uses of AbstractMOAObject in moa.classifiers.functions
Subclasses of AbstractMOAObject in moa.classifiers.functions Modifier and Type Class Description class
AdaGrad
Implements the AdaGrad oneline optimiser for learning various linear models (binary class SVM, binary class logistic regression and linear regression).class
MajorityClass
Majority class learner.class
NoChange
NoChange class classifier.class
Perceptron
Single perceptron classifier.class
SGD
Implements stochastic gradient descent for learning various linear models (binary class SVM, binary class logistic regression and linear regression).class
SGDMultiClass
Implements stochastic gradient descent for learning various linear models (binary class SVM, binary class logistic regression and linear regression).class
SPegasos
Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al. -
Uses of AbstractMOAObject in moa.classifiers.lazy
Subclasses of AbstractMOAObject in moa.classifiers.lazy Modifier and Type Class Description class
kNN
k Nearest Neighbor.class
kNNwithPAW
k Nearest Neighbor ADAPTIVE with PAW.class
kNNwithPAWandADWIN
k Nearest Neighbor ADAPTIVE with ADWIN+PAW.class
SAMkNN
Self Adjusting Memory (SAM) coupled with the k Nearest Neighbor classifier (kNN) . -
Uses of AbstractMOAObject in moa.classifiers.meta
Subclasses of AbstractMOAObject in moa.classifiers.meta Modifier and Type Class Description class
AccuracyUpdatedEnsemble
The revised version of the Accuracy Updated Ensemble as proposed by Brzezinski and Stefanowski in "Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm", IEEE Trans.class
AccuracyWeightedEnsemble
The Accuracy Weighted Ensemble classifier as proposed by Wang et al.class
ADACC
Anticipative and Dynamic Adaptation to Concept Changes.class
AdaptiveRandomForest
Adaptive Random Forestprotected class
AdaptiveRandomForest.ARFBaseLearner
Inner class that represents a single tree member of the forest.class
AdaptiveRandomForestRegressor
Implementation of AdaptiveRandomForestRegressor, an extension of AdaptiveRandomForest for classification.protected class
AdaptiveRandomForestRegressor.ARFFIMTDDBaseLearner
class
ADOB
Adaptable Diversity-based Online Boosting (ADOB) is a modified version of the online boosting, as proposed by Oza and Russell, which is aimed at speeding up the experts recovery after concept drifts.class
BOLE
class
DACC
Dynamic Adaptation to Concept Changes.class
DynamicWeightedMajority
Dynamic weighted majority algorithm.class
HeterogeneousEnsembleAbstract
BLAST (Best Last) for Heterogeneous Ensembles Abstract Base Classclass
HeterogeneousEnsembleBlast
BLAST (Best Last) for Heterogeneous Ensembles implemented with Fading Factorsclass
HeterogeneousEnsembleBlastFadingFactors
BLAST (Best Last) for Heterogeneous Ensembles implemented with Fading Factorsclass
LearnNSE
Ensemble of classifiers-based approach for incremental learning of concept drift, characterized by nonstationary environments (NSEs), where the underlying data distributions change over time.class
LeveragingBag
Leveraging Bagging for evolving data streams using ADWIN.class
LimAttClassifier
Ensemble Combining Restricted Hoeffding Trees using Stacking.class
MLCviaMTR
class
OCBoost
Online Coordinate boosting for two classes evolving data streams.class
OnlineAccuracyUpdatedEnsemble
The online version of the Accuracy Updated Ensemble as proposed by Brzezinski and Stefanowski in "Combining block-based and online methods in learning ensembles from concept drifting data streams", Information Sciences, 2014.class
OnlineSmoothBoost
Incremental on-line boosting with Theoretical Justifications of Shang-Tse Chen, Hsuan-Tien Lin and Chi-Jen Lu.class
OzaBag
Incremental on-line bagging of Oza and Russell.class
OzaBagAdwin
Bagging for evolving data streams using ADWIN.class
OzaBagASHT
Bagging using trees of different size.class
OzaBoost
Incremental on-line boosting of Oza and Russell.class
OzaBoostAdwin
Boosting for evolving data streams using ADWIN.class
PairedLearners
Creates two classifiers: a stable and a reactive.class
RandomRules
class
RCD
Creates a set of classifiers, each one representing a different context.class
SelfOptimisingKNearestLeaves
Implementation of Self-Optimising K Nearest Leaves.protected class
SelfOptimisingKNearestLeaves.SelfOptimisingKNearestLeavesBaseLearner
class
StreamingGradientBoostedTrees
Gradient boosted trees for evolving data streamsstatic class
StreamingGradientBoostedTrees.SGBT
class
StreamingRandomPatches
Streaming Random Patchesclass
TemporallyAugmentedClassifier
Include labels of previous instances into the training dataclass
WeightedMajorityAlgorithm
Weighted majority algorithm for data streams.class
WEKAClassifier
Class for using a classifier from WEKA. -
Uses of AbstractMOAObject in moa.classifiers.meta.imbalanced
Subclasses of AbstractMOAObject in moa.classifiers.meta.imbalanced Modifier and Type Class Description class
CSMOTE
CSMOTEclass
OnlineAdaBoost
Online AdaBoost is the online version of the boosting ensemble method AdaBoostclass
OnlineAdaC2
OnlineAdaC2 is the adaptation of the ensemble learner to data streamsclass
OnlineCSB2
Online CSB2 is the online version of the ensemble learner CSB2.class
OnlineRUSBoost
Online RUSBoost is the adaptation of the ensemble learner to data streams.class
OnlineSMOTEBagging
Online SMOTEBagging is the online version of the ensemble method SMOTEBagging.class
OnlineUnderOverBagging
Online UnderOverBagging is the online version of the ensemble method.class
RebalanceStream
RebalanceStream -
Uses of AbstractMOAObject in moa.classifiers.multilabel
Subclasses of AbstractMOAObject in moa.classifiers.multilabel Modifier and Type Class Description class
MajorityLabelset
Majority Labelset classifier.class
MEKAClassifier
Wrapper for MEKA classifiers.class
MultilabelHoeffdingTree
Hoeffding Tree for classifying multi-label data.static class
MultilabelHoeffdingTree.MultilabelInactiveLearningNode
class
MultilabelHoeffdingTree.MultilabelLearningNodeClassifier
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Uses of AbstractMOAObject in moa.classifiers.multilabel.core.splitcriteria
Subclasses of AbstractMOAObject in moa.classifiers.multilabel.core.splitcriteria Modifier and Type Class Description class
ICVarianceReduction
class
PCTWeightedICVarianceReduction
class
WeightedICVarianceReduction
Weighted intra cluster variance reduction split criterion -
Uses of AbstractMOAObject in moa.classifiers.multilabel.meta
Subclasses of AbstractMOAObject in moa.classifiers.multilabel.meta Modifier and Type Class Description class
OzaBagAdwinML
OzaBagAdwinML: Changes the way to compute accuracy as an input for Adwinclass
OzaBagML
OzaBag for Multi-label data. -
Uses of AbstractMOAObject in moa.classifiers.multilabel.trees
Subclasses of AbstractMOAObject in moa.classifiers.multilabel.trees Modifier and Type Class Description class
ISOUPTree
iSOUPTree class for structured output prediction.class
ISOUPTreeRF
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Uses of AbstractMOAObject in moa.classifiers.multitarget
Subclasses of AbstractMOAObject in moa.classifiers.multitarget Modifier and Type Class Description class
BasicMultiLabelClassifier
class
BasicMultiLabelLearner
Binary relevance Multilabel Classifierclass
BasicMultiTargetRegressor
Binary relevance Multi-Target Regressor -
Uses of AbstractMOAObject in moa.classifiers.multitarget.functions
Subclasses of AbstractMOAObject in moa.classifiers.multitarget.functions Modifier and Type Class Description class
MultiTargetNoChange
MultiTargetNoChange class regressor. -
Uses of AbstractMOAObject in moa.classifiers.oneclass
Subclasses of AbstractMOAObject in moa.classifiers.oneclass Modifier and Type Class Description class
Autoencoder
Implements an autoencoder: a neural network that attempts to reconstruct the input.class
HSTrees
Implements the Streaming Half-Space Trees one-class classifier described in S.class
NearestNeighbourDescription
Implements David Tax's Nearest Neighbour Description method described in Section 3.4.2 of D. -
Uses of AbstractMOAObject in moa.classifiers.rules
Subclasses of AbstractMOAObject in moa.classifiers.rules Modifier and Type Class Description class
AbstractAMRules
class
AMRulesRegressor
class
AMRulesRegressorOld
class
BinaryClassifierFromRegressor
Function that convertes a regressor into a binary classifier baseLearnerOption- regressor learner selectionclass
Predicates
class
RuleClassification
class
RuleClassifier
This classifier learn ordered and unordered rule set from data stream.class
RuleClassifierNBayes
This classifier learn ordered and unordered rule set from data stream with naive Bayes learners. -
Uses of AbstractMOAObject in moa.classifiers.rules.core
Subclasses of AbstractMOAObject in moa.classifiers.rules.core Modifier and Type Class Description class
NominalRulePredicate
Class that contains the literal information for a nominal variableclass
NumericRulePredicate
Class that contains the literal information for a numerical variableclass
Rule
class
RuleActiveLearningNode
A modified ActiveLearningNode that uses a Perceptron as the leaf node model, and ensures that the class values sent to the attribute observers are not truncated to ints if regression is being performedclass
RuleActiveRegressionNode
A modified ActiveLearningNode that uses a Perceptron as the leaf node model, and ensures that the class values sent to the attribute observers are not truncated to ints if regression is being performedclass
RuleSplitNode
A modified SplitNode method implementing the extra information -
Uses of AbstractMOAObject in moa.classifiers.rules.core.anomalydetection
Subclasses of AbstractMOAObject in moa.classifiers.rules.core.anomalydetection Modifier and Type Class Description class
AbstractAnomalyDetector
class
AnomalinessRatioScore
Score for anomaly detection percentageAnomalousAttributesOption - Percentage of anomalous attributes.class
NoAnomalyDetection
No anomaly detection is performedclass
OddsRatioScore
Score for anomaly detection: OddsRatio thresholdOption - The threshold value for detecting anomalies minNumberInstancesOption - The minimum number of instances required to perform anomaly detection probabilityFunctionOption - Probability function selection -
Uses of AbstractMOAObject in moa.classifiers.rules.core.anomalydetection.probabilityfunctions
Subclasses of AbstractMOAObject in moa.classifiers.rules.core.anomalydetection.probabilityfunctions Modifier and Type Class Description class
CantellisInequality
Returns the probability for anomaly detection according to a Cantelli inequality mean- mean of a data variable sd- standard deviation of a data variable value- current value of the variableclass
ChebyshevInequality
Returns the probability for anomaly detection according to a Chebyshev inequality mean- mean of a data variable sd- standard deviation of a data variable value- current value of the variableclass
GaussInequality
Returns the probability for anomaly detection according to a Gauss inequality mean- mean of a data variable sd- standard deviation of a data variable value- current value of the variable -
Uses of AbstractMOAObject in moa.classifiers.rules.core.attributeclassobservers
Subclasses of AbstractMOAObject in moa.classifiers.rules.core.attributeclassobservers Modifier and Type Class Description class
FIMTDDNumericAttributeClassLimitObserver
-
Uses of AbstractMOAObject in moa.classifiers.rules.core.changedetection
Subclasses of AbstractMOAObject in moa.classifiers.rules.core.changedetection Modifier and Type Class Description class
NoChangeDetection
-
Uses of AbstractMOAObject in moa.classifiers.rules.core.conditionaltests
Subclasses of AbstractMOAObject in moa.classifiers.rules.core.conditionaltests Modifier and Type Class Description class
NominalAttributeBinaryRulePredicate
Nominal binary conditional test for instances to use to split nodes in rules.class
NumericAttributeBinaryRulePredicate
Numeric binary conditional test for instances to use to split nodes in AMRules. -
Uses of AbstractMOAObject in moa.classifiers.rules.core.splitcriteria
Subclasses of AbstractMOAObject in moa.classifiers.rules.core.splitcriteria Modifier and Type Class Description class
SDRSplitCriterionAMRules
class
SDRSplitCriterionAMRulesNode
class
VarianceRatioSplitCriterion
class
VRSplitCriterion
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Uses of AbstractMOAObject in moa.classifiers.rules.core.voting
Subclasses of AbstractMOAObject in moa.classifiers.rules.core.voting Modifier and Type Class Description class
AbstractErrorWeightedVote
AbstractErrorWeightedVote class for weighted votes based on estimates of errors.class
ExpNegErrorWeightedVote
ExpNegErrorWeightedVote class for weighted votes based on estimates of errors.class
InverseErrorWeightedVote
InverseErrorWeightedVoteMultiLabel class for weighted votes based on estimates of errors.class
MinErrorWeightedVote
MinErrorWeightedVote class for weighted votes based on estimates of errors.class
OneMinusErrorWeightedVote
class
UniformWeightedVote
UniformWeightedVote class for weighted votes based on estimates of errors. -
Uses of AbstractMOAObject in moa.classifiers.rules.errormeasurers
Subclasses of AbstractMOAObject in moa.classifiers.rules.errormeasurers Modifier and Type Class Description class
ErrorMeasurement
Computes error measures with a fading factor fadingErrorFactorOption - Fading factorclass
MeanAbsoluteDeviation
Computes the Mean Absolute Deviation for single target regression problemsclass
RootMeanSquaredError
Computes the Root Mean Squared Error for single target regression problems -
Uses of AbstractMOAObject in moa.classifiers.rules.featureranking
Subclasses of AbstractMOAObject in moa.classifiers.rules.featureranking Modifier and Type Class Description class
AbstractFeatureRanking
class
BasicFeatureRanking
Basic Feature Ranking method João Duarte, João Gama,Feature ranking in hoeffding algorithms for regression.class
MeritFeatureRanking
Merit Feature Ranking method João Duarte, João Gama,Feature ranking in hoeffding algorithms for regression.class
NoFeatureRanking
No feature ranking is performedclass
WeightedMajorityFeatureRanking
Weighted Majority Feature Ranking method João Duarte, João Gama,Feature ranking in hoeffding algorithms for regression. -
Uses of AbstractMOAObject in moa.classifiers.rules.functions
Subclasses of AbstractMOAObject in moa.classifiers.rules.functions Modifier and Type Class Description class
AdaptiveNodePredictor
class
FadingTargetMean
class
LowPassFilteredLearner
class
Perceptron
class
TargetMean
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Uses of AbstractMOAObject in moa.classifiers.rules.meta
Subclasses of AbstractMOAObject in moa.classifiers.rules.meta Modifier and Type Class Description class
RandomAMRules
Random AMRules algoritgm that performs analogous procedure as the Random Forest Trees but with Rulesclass
RandomAMRulesOld
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Uses of AbstractMOAObject in moa.classifiers.rules.multilabel
Subclasses of AbstractMOAObject in moa.classifiers.rules.multilabel Modifier and Type Class Description class
AMRulesMultiLabelClassifier
Method for online multi-Label classification.class
AMRulesMultiLabelLearner
Adaptive Model Rules for MultiLabel problems (AMRulesML), the streaming rule learning algorithm.class
AMRulesMultiLabelLearnerSemiSuper
Semi-supervised method for online multi-target regression.class
AMRulesMultiTargetRegressor
AMRules Algorithm for multitarget splitCriterionOption- Split criterion used to assess the merit of a split weightedVoteOption - Weighted vote type learnerOption - Learner selection errorMeasurerOption - Measure of error for deciding which learner should predict changeDetector - Change selection João Duarte, João Gama, Albert Bifet, Adaptive Model Rules From High-Speed Data Streams.class
AMRulesMultiTargetRegressorSemiSuper
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Uses of AbstractMOAObject in moa.classifiers.rules.multilabel.attributeclassobservers
Subclasses of AbstractMOAObject in moa.classifiers.rules.multilabel.attributeclassobservers Modifier and Type Class Description class
MultiLabelBSTree
Binary search tree for AMRules splitting points determinationclass
MultiLabelBSTreeFloat
class
MultiLabelBSTreePCT
class
MultiLabelNominalAttributeObserver
Function for determination of splitting points for nominal variablesclass
SingleVector
Vector of float numbers with some utilities. -
Uses of AbstractMOAObject in moa.classifiers.rules.multilabel.core
Subclasses of AbstractMOAObject in moa.classifiers.rules.multilabel.core Modifier and Type Class Description class
AttributeExpansionSuggestion
Class for computing attribute split suggestions given a split test.class
LearningLiteral
class
LearningLiteralClassification
This class contains the functions for learning the literals for Multi-label classification (in same way as Multi-Target regression).class
LearningLiteralRegression
class
Literal
class
MultiLabelRule
class
MultiLabelRuleClassification
class
MultiLabelRuleRegression
class
ObservableMOAObject
-
Uses of AbstractMOAObject in moa.classifiers.rules.multilabel.core.splitcriteria
Subclasses of AbstractMOAObject in moa.classifiers.rules.multilabel.core.splitcriteria Modifier and Type Class Description class
MultilabelInformationGain
Multi-label Information Gain.class
MultiTargetVarianceRatio
-
Uses of AbstractMOAObject in moa.classifiers.rules.multilabel.core.voting
Subclasses of AbstractMOAObject in moa.classifiers.rules.multilabel.core.voting Modifier and Type Class Description class
AbstractErrorWeightedVoteMultiLabel
AbstractErrorWeightedVote class for weighted votes based on estimates of errors.class
FirstHitVoteMultiLabel
FirstHitVoteMultiLabel class for weighted votes based on estimates of errors.class
InverseErrorWeightedVoteMultiLabel
InverseErrorWeightedVoteMuliLabel class for weighted votes based on estimates of errors.class
UniformWeightedVoteMultiLabel
UniformWeightedVote class for weighted votes based on estimates of errors. -
Uses of AbstractMOAObject in moa.classifiers.rules.multilabel.errormeasurers
Subclasses of AbstractMOAObject in moa.classifiers.rules.multilabel.errormeasurers Modifier and Type Class Description class
AbstractMultiLabelErrorMeasurer
class
AbstractMultiTargetErrorMeasurer
class
MeanAbsoluteDeviationMT
Mean Absolute Deviation for multitarget and with fading factorclass
RelativeMeanAbsoluteDeviationMT
Relative Mean Absolute Deviation for multitarget and with fading factorclass
RelativeRootMeanSquaredErrorMT
Relative Root Mean Squared Error for multitarget and with fading factorclass
RootMeanSquaredErrorMT
Root Mean Squared Error for multitarget and with fading factor -
Uses of AbstractMOAObject in moa.classifiers.rules.multilabel.functions
Subclasses of AbstractMOAObject in moa.classifiers.rules.multilabel.functions Modifier and Type Class Description class
AbstractAMRulesFunctionBasicMlLearner
class
AdaptiveMultiTargetRegressor
Adaptive MultiTarget Regressor uses two learner The first is used in first stage when high error are produced(e.g.class
DominantLabelsClassifier
class
MultiLabelNaiveBayes
Binary relevance with Naive Bayesclass
MultiLabelPerceptronClassification
Multi-Label perceptron classifier (by Binary Relevance).class
MultiTargetMeanRegressor
Target mean regressorclass
MultiTargetPerceptronRegressor
Binary relevance with a regression perceptronclass
StackedPredictor
-
Uses of AbstractMOAObject in moa.classifiers.rules.multilabel.inputselectors
Subclasses of AbstractMOAObject in moa.classifiers.rules.multilabel.inputselectors Modifier and Type Class Description class
MeritThreshold
Input selection algorithm based on Merit thresholdclass
SelectAllInputs
Does not selects inputs -
Uses of AbstractMOAObject in moa.classifiers.rules.multilabel.instancetransformers
Subclasses of AbstractMOAObject in moa.classifiers.rules.multilabel.instancetransformers Modifier and Type Class Description class
InstanceAttributesSelector
Transforms instances considering both a subset of input attributes and a subset of output attributesclass
InstanceOutputAttributesSelector
Transforms instances considering only a subset of output attributesclass
NoInstanceTransformation
Performs no transformation. -
Uses of AbstractMOAObject in moa.classifiers.rules.multilabel.meta
Subclasses of AbstractMOAObject in moa.classifiers.rules.multilabel.meta Modifier and Type Class Description class
MultiLabelRandomAMRules
-
Uses of AbstractMOAObject in moa.classifiers.rules.multilabel.outputselectors
Subclasses of AbstractMOAObject in moa.classifiers.rules.multilabel.outputselectors Modifier and Type Class Description class
EntropyThreshold
Entropy measure use by online multi-label AMRules for heuristics computation.class
SelectAllOutputs
class
StdDevThreshold
class
VarianceThreshold
-
Uses of AbstractMOAObject in moa.classifiers.trees
-
Uses of AbstractMOAObject in moa.classifiers.trees.iadem
Subclasses of AbstractMOAObject in moa.classifiers.trees.iadem Modifier and Type Class Description class
Iadem2
class
Iadem3
class
Iadem3Subtree
class
IademAttributeSplitSuggestion
class
IademGaussianNumericAttributeClassObserver
class
IademGreenwaldKhannaNumericAttributeClassObserver
class
IademGreenwaldKhannaQuantileSummary
class
IademNominalAttributeBinaryTest
class
IademNominalAttributeMultiwayTest
class
IademNumericAttributeBinaryTest
class
IademVFMLNumericAttributeClassObserver
-
Uses of AbstractMOAObject in moa.cluster
Subclasses of AbstractMOAObject in moa.cluster Modifier and Type Class Description class
CFCluster
class
Cluster
class
Clustering
class
SphereCluster
A simple implementation of theCluster
interface representing spherical clusters. -
Uses of AbstractMOAObject in moa.clusterers
Subclasses of AbstractMOAObject in moa.clusterers Modifier and Type Class Description class
AbstractClusterer
class
ClusterGenerator
class
CobWeb
Class implementing the Cobweb and Classit clustering algorithms.class
WekaClusteringAlgorithm
-
Uses of AbstractMOAObject in moa.clusterers.clustream
Subclasses of AbstractMOAObject in moa.clusterers.clustream Modifier and Type Class Description class
Clustream
Citation: CluStream: Charu C.class
ClustreamKernel
class
WithKmeans
-
Uses of AbstractMOAObject in moa.clusterers.clustree
Subclasses of AbstractMOAObject in moa.clusterers.clustree Modifier and Type Class Description class
ClusKernel
Representation of an Entry in the treeclass
ClusTree
Citation: ClusTree: Philipp Kranen, Ira Assent, Corinna Baldauf, Thomas Seidl: The ClusTree: indexing micro-clusters for anytime stream mining. -
Uses of AbstractMOAObject in moa.clusterers.denstream
Subclasses of AbstractMOAObject in moa.clusterers.denstream Modifier and Type Class Description class
MicroCluster
class
Timestamp
class
WithDBSCAN
-
Uses of AbstractMOAObject in moa.clusterers.dstream
Subclasses of AbstractMOAObject in moa.clusterers.dstream Modifier and Type Class Description class
DensityGrid
Density Grids are defined in equation 3 (section 3.1) of Chen and Tu 2007 as: In D-Stream, we partition the d−dimensional space S into density grids.class
Dstream
Citation: Y.class
GridCluster
Grid Clusters are defined in Definition 3.6 of Chen and Tu 2007 as: Let G =(g1, ·· · ,gm) be a grid group, if every inside grid of G is a dense grid and every outside grid is either a dense grid or a transitional grid, then G is a grid cluster. -
Uses of AbstractMOAObject in moa.clusterers.kmeanspm
Subclasses of AbstractMOAObject in moa.clusterers.kmeanspm Modifier and Type Class Description class
BICO
A instance of this class provides the BICO clustering algorithm.class
ClusteringFeature
Provides a ClusteringFeature.class
ClusteringTreeHeadNode
Provides a ClusteringTreeNode with an extended nearest neighbor search in the root.class
ClusteringTreeNode
Provides a tree of ClusterFeatures. -
Uses of AbstractMOAObject in moa.clusterers.macro
Subclasses of AbstractMOAObject in moa.clusterers.macro Modifier and Type Class Description class
NonConvexCluster
-
Uses of AbstractMOAObject in moa.clusterers.meta
Subclasses of AbstractMOAObject in moa.clusterers.meta Modifier and Type Class Description class
ConfStream
class
EnsembleClustererAbstract
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Uses of AbstractMOAObject in moa.clusterers.outliers
Subclasses of AbstractMOAObject in moa.clusterers.outliers Modifier and Type Class Description class
MyBaseOutlierDetector
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Uses of AbstractMOAObject in moa.clusterers.outliers.AbstractC
Subclasses of AbstractMOAObject in moa.clusterers.outliers.AbstractC Modifier and Type Class Description class
AbstractC
class
AbstractCBase
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Uses of AbstractMOAObject in moa.clusterers.outliers.Angiulli
Subclasses of AbstractMOAObject in moa.clusterers.outliers.Angiulli Modifier and Type Class Description class
ApproxSTORM
class
ExactSTORM
class
STORMBase
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Uses of AbstractMOAObject in moa.clusterers.outliers.AnyOut
Subclasses of AbstractMOAObject in moa.clusterers.outliers.AnyOut Modifier and Type Class Description class
AnyOut
class
AnyOutCore
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Uses of AbstractMOAObject in moa.clusterers.outliers.MCOD
Subclasses of AbstractMOAObject in moa.clusterers.outliers.MCOD Modifier and Type Class Description class
MCOD
class
MCODBase
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Uses of AbstractMOAObject in moa.clusterers.outliers.SimpleCOD
Subclasses of AbstractMOAObject in moa.clusterers.outliers.SimpleCOD Modifier and Type Class Description class
SimpleCOD
class
SimpleCODBase
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Uses of AbstractMOAObject in moa.clusterers.streamkm
Subclasses of AbstractMOAObject in moa.clusterers.streamkm Modifier and Type Class Description class
StreamKM
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Uses of AbstractMOAObject in moa.core
Subclasses of AbstractMOAObject in moa.core Modifier and Type Class Description class
DoubleVector
Vector of double numbers with some utilities.class
GaussianEstimator
Gaussian incremental estimator that uses incremental method that is more resistant to floating point imprecision.class
GreenwaldKhannaQuantileSummary
Class for representing summaries of Greenwald and Khanna quantiles.class
Measurement
Class for storing an evaluation measurement. -
Uses of AbstractMOAObject in moa.core.utils
Subclasses of AbstractMOAObject in moa.core.utils Modifier and Type Class Description class
Converter
Converter. -
Uses of AbstractMOAObject in moa.evaluation
Subclasses of AbstractMOAObject in moa.evaluation Modifier and Type Class Description class
Accuracy
class
AdwinClassificationPerformanceEvaluator
Classification evaluator that updates evaluation results using an adaptive sliding window.class
ALMeasureCollection
Collection of measures used to evaluate AL tasks.class
ALWindowClassificationPerformanceEvaluator
Active Learning Wrapper for BasicClassificationPerformanceEvaluator.class
BasicAUCImbalancedPerformanceEvaluator
Performance measures designed for class imbalance problems.class
BasicClassificationPerformanceEvaluator
Classification evaluator that performs basic incremental evaluation.class
BasicConceptDriftPerformanceEvaluator
class
BasicMultiLabelPerformanceEvaluator
Multilabel Window Classification Performance Evaluator.class
BasicMultiTargetPerformanceEvaluator
Regression evaluator that performs basic incremental evaluation.class
BasicMultiTargetPerformanceRelativeMeasuresEvaluator
Regression evaluator that performs basic incremental evaluation.class
BasicRegressionPerformanceEvaluator
Regression evaluator that performs basic incremental evaluation.class
ChangeDetectionMeasures
class
CMM
class
EntropyCollection
class
EWMAClassificationPerformanceEvaluator
Classification evaluator that updates evaluation results using an Exponential Weighted Moving Average.class
F1
class
FadingFactorClassificationPerformanceEvaluator
Classification evaluator that updates evaluation results using a fading factor.class
General
class
LearningEvaluation
Class that stores an array of evaluation measurements.class
MeasureCollection
class
MultiTargetWindowRegressionPerformanceEvaluator
Multi-target regression evaluator that updates evaluation results using a sliding window.class
MultiTargetWindowRegressionPerformanceRelativeMeasuresEvaluator
Multi-target regression evaluator that updates evaluation results using a sliding window.class
OutlierPerformance
class
RegressionAccuracy
class
Separation
class
SilhouetteCoefficient
class
SSQ
class
StatisticalCollection
class
WindowAUCImbalancedPerformanceEvaluator
Classification evaluator that updates evaluation results using a sliding window.class
WindowClassificationPerformanceEvaluator
Classification evaluator that updates evaluation results using a sliding window.class
WindowRegressionPerformanceEvaluator
Regression evaluator that updates evaluation results using a sliding window. -
Uses of AbstractMOAObject in moa.evaluation.preview
Subclasses of AbstractMOAObject in moa.evaluation.preview Modifier and Type Class Description class
LearningCurve
Class that stores and keeps the history of evaluation measurements.class
Preview
Abstract class which is used to define the methods needed from a previewclass
PreviewCollection<CollectionElementType extends Preview>
Class that stores and keeps the history of multiple previewsclass
PreviewCollectionLearningCurveWrapper
Class used to wrap LearningCurve so that it can be used in conjunction with a PreviewCollection -
Uses of AbstractMOAObject in moa.gui.experimentertab.tasks
Subclasses of AbstractMOAObject in moa.gui.experimentertab.tasks Modifier and Type Class Description class
ConceptDriftMainTask
class
EvaluateConceptDrift
Task for evaluating a classifier on a stream by testing then training with each example in sequence.class
EvaluateInterleavedChunks
class
EvaluateInterleavedTestThenTrain
Task for evaluating a classifier on a stream by testing then training with each example in sequence.class
EvaluatePeriodicHeldOutTest
Task for evaluating a classifier on a stream by periodically testing on a heldout set.class
EvaluatePrequential
class
EvaluatePrequentialCV
Task for prequential cross-validation evaluation of a classifier on a stream by testing then training with each example in sequence and doing cross-validation at the same time.class
ExperimenterTask
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Uses of AbstractMOAObject in moa.learners
Subclasses of AbstractMOAObject in moa.learners Modifier and Type Class Description class
ChangeDetectorLearner
Class for detecting concept drift and to be used as a learner. -
Uses of AbstractMOAObject in moa.learners.featureanalysis
Subclasses of AbstractMOAObject in moa.learners.featureanalysis Modifier and Type Class Description class
ClassifierWithFeatureImportance
Classifier with Feature Importanceclass
FeatureImportanceHoeffdingTree
HoeffdingTree Feature Importance extends the traditional HoeffdingTree classifier to also yield feature importances.class
FeatureImportanceHoeffdingTreeEnsemble
HoeffdingTree Ensemble Feature Importance. -
Uses of AbstractMOAObject in moa.options
Subclasses of AbstractMOAObject in moa.options Modifier and Type Class Description class
AbstractOptionHandler
Abstract Option Handler. -
Uses of AbstractMOAObject in moa.recommender.data
Subclasses of AbstractMOAObject in moa.recommender.data Modifier and Type Class Description class
MemRecommenderData
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Uses of AbstractMOAObject in moa.recommender.dataset.impl
Subclasses of AbstractMOAObject in moa.recommender.dataset.impl Modifier and Type Class Description class
FlixsterDataset
class
JesterDataset
class
MovielensDataset
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Uses of AbstractMOAObject in moa.recommender.predictor
Subclasses of AbstractMOAObject in moa.recommender.predictor Modifier and Type Class Description class
BaselinePredictor
A naive algorithm which combines the global mean of all the existing ratings, the mean rating of the user and the mean rating of the item to make a prediction.class
BRISMFPredictor
Implementation of the algorithm described in Scalable Collaborative Filtering Approaches for Large Recommender Systems (Gábor Takács, István Pilászy, Bottyán Németh, and Domonkos Tikk). -
Uses of AbstractMOAObject in moa.streams
Subclasses of AbstractMOAObject in moa.streams Modifier and Type Class Description class
ArffFileStream
Stream reader of ARFF files.class
BootstrappedStream
Bootstrapped Streamclass
CachedInstancesStream
Stream generator for representing a stream that is cached in memory.class
ConceptDriftRealStream
Stream generator that adds concept drift to examples in a stream with different classes and attributes.class
ConceptDriftStream
Stream generator that adds concept drift to examples in a stream.class
FilteredStream
Class for representing a stream that is filtered.class
ImbalancedStream
Imbalanced Stream.class
IrrelevantFeatureAppenderStream
IrrelevantFeatureAppender Stream.class
MultiFilteredStream
Class for representing a stream that is filtered.class
MultiLabelFilteredStream
Class for representing a stream that is filtered.class
MultiTargetArffFileStream
Stream reader of ARFF files.class
PartitioningStream
This stream partitions the base stream into n distinct streams and outputs one of themclass
RecurrentConceptDriftStream
Stream generator that adds recurrent concept drifts to examples in a stream. -
Uses of AbstractMOAObject in moa.streams.clustering
Subclasses of AbstractMOAObject in moa.streams.clustering Modifier and Type Class Description class
ClusteringStream
class
FileStream
class
RandomRBFGeneratorEvents
class
SimpleCSVStream
Provides a simple input stream for csv files. -
Uses of AbstractMOAObject in moa.streams.filters
Subclasses of AbstractMOAObject in moa.streams.filters Modifier and Type Class Description class
AbstractMultiLabelStreamFilter
Abstract Stream Filter.class
AbstractStreamFilter
Abstract Stream Filter.class
AddNoiseFilter
Filter for adding random noise to examples in a stream.class
HashingTrickFilter
Filter to perform feature hashing to reduce the number of attributes by applying a hash function to features.class
NormalisationFilter
Filter for standardising and normalising instances in a stream.class
RandomProjectionFilter
Filter to perform random projection to reduce the number of attributes.class
RBFFilter
class
ReLUFilter
class
RemoveDiscreteAttributeFilter
Filter for removing discrete attributes in instances of a stream.class
ReplacingMissingValuesFilter
Replaces the missing values with another value according to the selected strategy.class
SelectAttributesFilter
class
StandardisationFilter
This filter is to standardise instances in a stream. -
Uses of AbstractMOAObject in moa.streams.generators
Subclasses of AbstractMOAObject in moa.streams.generators Modifier and Type Class Description class
AgrawalGenerator
Stream generator for Agrawal dataset.class
AssetNegotiationGenerator
class
HyperplaneGenerator
Stream generator for Hyperplane data stream.class
LEDGenerator
Stream generator for the problem of predicting the digit displayed on a 7-segment LED display.class
LEDGeneratorDrift
Stream generator for the problem of predicting the digit displayed on a 7-segment LED display with drift.class
MixedGenerator
Abrupt concept drift, boolean noise-free examples.class
RandomRBFGenerator
Stream generator for a random radial basis function stream.class
RandomRBFGeneratorDrift
Stream generator for a random radial basis function stream with drift.class
RandomTreeGenerator
Stream generator for a stream based on a randomly generated tree..class
SEAGenerator
Stream generator for SEA concepts functions.class
SineGenerator
1.SINE1.class
STAGGERGenerator
Stream generator for STAGGER Concept functions.class
TextGenerator
Text generator that simulates sentiment analysis on tweets.class
WaveformGenerator
Stream generator for the problem of predicting one of three waveform types.class
WaveformGeneratorDrift
Stream generator for the problem of predicting one of three waveform types with drift. -
Uses of AbstractMOAObject in moa.streams.generators.cd
Subclasses of AbstractMOAObject in moa.streams.generators.cd Modifier and Type Class Description class
AbruptChangeGenerator
class
AbstractConceptDriftGenerator
class
GradualChangeGenerator
class
NoChangeGenerator
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Uses of AbstractMOAObject in moa.streams.generators.multilabel
Subclasses of AbstractMOAObject in moa.streams.generators.multilabel Modifier and Type Class Description class
MetaMultilabelGenerator
Stream generator for multilabel data.class
MultilabelArffFileStream
Stream reader for ARFF files of multilabel data. -
Uses of AbstractMOAObject in moa.tasks
Subclasses of AbstractMOAObject in moa.tasks Modifier and Type Class Description class
AbstractTask
Abstract Task.class
AuxiliarMainTask
Abstract Auxiliar Main Task.class
CacheShuffledStream
Task for storing and shuffling examples in memory.class
ClassificationMainTask
Abstract Classification Main Task.class
ConceptDriftMainTask
class
EvaluateClustering
Task for evaluating a clusterer on a stream.class
EvaluateConceptDrift
Task for evaluating a classifier on a stream by testing then training with each example in sequence.class
EvaluateInterleavedChunks
class
EvaluateInterleavedTestThenTrain
Task for evaluating a classifier on a stream by testing then training with each example in sequence.class
EvaluateModel
Task for evaluating a static model on a stream.class
EvaluateModelMultiLabel
Task for evaluating a static model on a stream.class
EvaluateModelMultiTarget
Task for evaluating a static model on a stream.class
EvaluateModelRegression
Task for evaluating a static model on a stream.class
EvaluateMultipleClusterings
Task for evaluating a clusterer on multiple (related) streams.class
EvaluateOnlineRecommender
Test for evaluating a recommender by training and periodically testing on samples from a rating dataset.class
EvaluatePeriodicHeldOutTest
Task for evaluating a classifier on a stream by periodically testing on a heldout set.class
EvaluatePrequential
Task for evaluating a classifier on a stream by testing then training with each example in sequence.class
EvaluatePrequentialCV
Task for prequential cross-validation evaluation of a classifier on a stream by testing then training with each example in sequence and doing cross-validation at the same time.class
EvaluatePrequentialDelayed
Task for evaluating a classifier on a delayed stream by testing and only training with the example after k other examples (delayed labeling).class
EvaluatePrequentialDelayedCV
Task for delayed cross-validation evaluation of a classifier on a stream by testing and only training with the example after the arrival of other k examples (delayed labeling).class
EvaluatePrequentialMultiLabel
Task for evaluating a classifier on a stream by testing then training with each example in sequence.class
EvaluatePrequentialMultiTarget
Task for evaluating a classifier on a stream by testing then training with each example in sequence.class
EvaluatePrequentialMultiTargetSemiSuper
Multi-target Prequential semi-supervised evaluation Phase1: Creates a initial model withof the instances in the dataset Phase2: When an instance is received: A binary random process with a binomial distribution selects if the instance should be labeled or unlabeled with probability . class
EvaluatePrequentialRegression
Task for evaluating a classifier on a stream by testing then training with each example in sequence.class
FailedTaskReport
Class for reporting a failed task.class
FeatureImportanceConfig
This class Provides GUI to user so that they can configure parameters for feature importance algorithm.class
LearnModel
Task for learning a model without any evaluation.class
LearnModelMultiLabel
Task for learning a model without any evaluation.class
LearnModelMultiTarget
Task for learning a model without any evaluation.class
LearnModelRegression
Task for learning a model without any evaluation.class
MainTask
Abstract Main Task.class
MeasureStreamSpeed
Task for measuring the speed of the stream.class
MultiLabelMainTask
class
MultiTargetMainTask
class
Plot
A task allowing to create and plot gnuplot scripts.class
RegressionMainTask
Abstract Regression Main Task.class
RunStreamTasks
Task for running several experiments modifying values of parameters.class
RunTasks
Task for running several experiments modifying values of parameters.class
WriteConfigurationToJupyterNotebook
Export the configuration of an training method form MOA to a IPYNB fileclass
WriteMultipleStreamsToARFF
Task to output multiple streams to a ARFF files using different random seedsclass
WriteStreamToARFFFile
Task to output a stream to an ARFF file -
Uses of AbstractMOAObject in moa.tasks.meta
Subclasses of AbstractMOAObject in moa.tasks.meta Modifier and Type Class Description class
ALMainTask
This class provides a superclass for Active Learning tasks, which enables convenient searching for those tasks for example when showing a list of available Active Learning tasks.class
ALMultiParamTask
This task individually evaluates an active learning classifier for each element of a set of parameter values.class
ALPartitionEvaluationTask
This task extensively evaluates an active learning classifier on a stream.class
ALPrequentialEvaluationTask
This task performs prequential evaluation for an active learning classifier (testing, then training with each example in sequence).class
MetaMainTask
This class provides features for handling tasks in a tree-like structure of parents and subtasks.
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