Uses of Interface
moa.learners.Learner
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Uses of Learner in moa.classifiers
Subinterfaces of Learner in moa.classifiers Modifier and Type Interface Description interface
Classifier
Classifier interface for incremental classification models.interface
MultiLabelClassifier
interface
MultiLabelLearner
interface
MultiTargetLearnerSemiSupervised
interface
MultiTargetRegressor
MultiTargetRegressor interface for incremental MultiTarget regression models.Classes in moa.classifiers that implement Learner Modifier and Type Class Description class
AbstractClassifier
class
AbstractMultiLabelLearner
Methods in moa.classifiers that return Learner Modifier and Type Method Description Learner[]
AbstractClassifier. getSublearners()
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Uses of Learner in moa.classifiers.active
Subinterfaces of Learner in moa.classifiers.active Modifier and Type Interface Description interface
ALClassifier
Active Learning Classifier Interface to make AL Classifiers selectable in AL tasks.Classes in moa.classifiers.active that implement Learner Modifier and Type Class Description class
ALRandom
class
ALUncertainty
Active learning setting for evolving data streams. -
Uses of Learner in moa.classifiers.bayes
Classes in moa.classifiers.bayes that implement Learner 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 Learner in moa.classifiers.deeplearning
Classes in moa.classifiers.deeplearning that implement Learner Modifier and Type Class Description class
CAND
Continuously Adaptive Neural networks for Data streamsclass
MLP
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Uses of Learner in moa.classifiers.drift
Classes in moa.classifiers.drift that implement Learner 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 Learner in moa.classifiers.functions
Classes in moa.classifiers.functions that implement Learner 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 Learner in moa.classifiers.lazy
Classes in moa.classifiers.lazy that implement Learner 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 Learner in moa.classifiers.meta
Classes in moa.classifiers.meta that implement Learner 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 Forestclass
AdaptiveRandomForestRegressor
Implementation of AdaptiveRandomForestRegressor, an extension of AdaptiveRandomForest for classification.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.class
StreamingGradientBoostedTrees
Gradient boosted trees for evolving data streamsclass
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 Learner in moa.classifiers.meta.imbalanced
Classes in moa.classifiers.meta.imbalanced that implement Learner 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 Learner in moa.classifiers.multilabel
Classes in moa.classifiers.multilabel that implement Learner 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. -
Uses of Learner in moa.classifiers.multilabel.meta
Classes in moa.classifiers.multilabel.meta that implement Learner 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 Learner in moa.classifiers.multilabel.trees
Classes in moa.classifiers.multilabel.trees that implement Learner Modifier and Type Class Description class
ISOUPTree
iSOUPTree class for structured output prediction.class
ISOUPTreeRF
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Uses of Learner in moa.classifiers.multitarget
Classes in moa.classifiers.multitarget that implement Learner Modifier and Type Class Description class
BasicMultiLabelClassifier
class
BasicMultiLabelLearner
Binary relevance Multilabel Classifierclass
BasicMultiTargetRegressor
Binary relevance Multi-Target Regressor -
Uses of Learner in moa.classifiers.multitarget.functions
Classes in moa.classifiers.multitarget.functions that implement Learner Modifier and Type Class Description class
MultiTargetNoChange
MultiTargetNoChange class regressor. -
Uses of Learner in moa.classifiers.oneclass
Classes in moa.classifiers.oneclass that implement Learner 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 Learner in moa.classifiers.rules
Classes in moa.classifiers.rules that implement Learner 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
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 Learner in moa.classifiers.rules.functions
Subinterfaces of Learner in moa.classifiers.rules.functions Modifier and Type Interface Description interface
AMRulesLearner
interface
AMRulesRegressorFunction
Classes in moa.classifiers.rules.functions that implement Learner Modifier and Type Class Description class
AdaptiveNodePredictor
class
FadingTargetMean
class
LowPassFilteredLearner
class
Perceptron
class
TargetMean
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Uses of Learner in moa.classifiers.rules.meta
Classes in moa.classifiers.rules.meta that implement Learner 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 Learner in moa.classifiers.rules.multilabel
Classes in moa.classifiers.rules.multilabel that implement Learner 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 Learner in moa.classifiers.rules.multilabel.functions
Classes in moa.classifiers.rules.multilabel.functions that implement Learner 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
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Uses of Learner in moa.classifiers.rules.multilabel.meta
Classes in moa.classifiers.rules.multilabel.meta that implement Learner Modifier and Type Class Description class
MultiLabelRandomAMRules
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Uses of Learner in moa.classifiers.trees
Classes in moa.classifiers.trees that implement Learner Modifier and Type Class Description class
AdaHoeffdingOptionTree
Adaptive decision option tree for streaming data with adaptive Naive Bayes classification at leaves.class
ARFFIMTDD
Implementation of ARFFIMTDD, an extension of FIMTDD to be used by AdaptiveRandomForestRegressor.class
ARFHoeffdingTree
Adaptive Random Forest Hoeffding Tree.class
ASHoeffdingTree
Adaptive Size Hoeffding Tree used in Bagging using trees of different size.class
DecisionStump
Decision trees of one level.
Parameters:class
EFDT
class
FIMTDD
Implementation of FIMTDD, regression and model trees for data streams.class
HoeffdingAdaptiveTree
Hoeffding Adaptive Tree for evolving data streams.class
HoeffdingAdaptiveTreeClassifLeaves
Hoeffding Adaptive Tree for evolving data streams that has a classifier at the leaves.class
HoeffdingOptionTree
Hoeffding Option Tree.class
HoeffdingTree
Hoeffding Tree or VFDT.class
HoeffdingTreeClassifLeaves
Hoeffding Tree that have a classifier at the leaves.class
LimAttHoeffdingTree
Hoeffding decision trees with a restricted number of attributes for data streams.class
ORTO
class
RandomHoeffdingTree
Random decision trees for data streams.class
SelfOptimisingBaseTree
See details in:
Yibin Sun, Bernhard Pfahringer, Heitor Murilo Gomes, Albert Bifet. -
Uses of Learner in moa.classifiers.trees.iadem
Classes in moa.classifiers.trees.iadem that implement Learner Modifier and Type Class Description class
Iadem2
class
Iadem3
class
Iadem3Subtree
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Uses of Learner in moa.evaluation
Constructors in moa.evaluation with parameters of type Learner Constructor Description LearningEvaluation(Measurement[] evaluationMeasurements, LearningPerformanceEvaluator cpe, Learner model)
LearningEvaluation(LearningPerformanceEvaluator cpe, Learner model)
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Uses of Learner in moa.learners
Subinterfaces of Learner in moa.learners Modifier and Type Interface Description interface
LearnerSemiSupervised<E extends Example>
Classes in moa.learners that implement Learner Modifier and Type Class Description class
ChangeDetectorLearner
Class for detecting concept drift and to be used as a learner.Methods in moa.learners that return Learner Modifier and Type Method Description Learner[]
Learner. getSublearners()
Gets the learners of this ensemble. -
Uses of Learner in moa.learners.featureanalysis
Subinterfaces of Learner in moa.learners.featureanalysis Modifier and Type Interface Description interface
FeatureImportanceClassifier
Feature Importance ClassifierClasses in moa.learners.featureanalysis that implement Learner 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.
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