Uses of Interface
com.github.javacliparser.Configurable
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Uses of Configurable in moa.classifiers
Subinterfaces of Configurable 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 Configurable Modifier and Type Class Description class
AbstractClassifier
class
AbstractMultiLabelLearner
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Uses of Configurable in moa.classifiers.active
Subinterfaces of Configurable 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 Configurable Modifier and Type Class Description class
ALRandom
class
ALUncertainty
Active learning setting for evolving data streams. -
Uses of Configurable in moa.classifiers.active.budget
Classes in moa.classifiers.active.budget that implement Configurable Modifier and Type Class Description class
FixedBM
-
Uses of Configurable in moa.classifiers.bayes
Classes in moa.classifiers.bayes that implement Configurable 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 Configurable in moa.classifiers.core.attributeclassobservers
Subinterfaces of Configurable in moa.classifiers.core.attributeclassobservers Modifier and Type Interface Description interface
AttributeClassObserver
Interface for observing the class data distribution for an attribute.interface
DiscreteAttributeClassObserver
Interface for observing the class data distribution for a discrete (nominal) attribute.interface
NumericAttributeClassObserver
Interface for observing the class data distribution for a numeric attribute.Classes in moa.classifiers.core.attributeclassobservers that implement Configurable 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 Configurable in moa.classifiers.core.driftdetection
Subinterfaces of Configurable in moa.classifiers.core.driftdetection Modifier and Type Interface Description interface
ChangeDetector
Change Detector interface to implement methods that detects change.Classes in moa.classifiers.core.driftdetection that implement Configurable Modifier and Type Class Description class
AbstractChangeDetector
Abstract Change Detector.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
SeqDrift2ChangeDetector
SeqDriftChangeDetector.java.class
STEPD
-
Uses of Configurable in moa.classifiers.core.splitcriteria
Subinterfaces of Configurable in moa.classifiers.core.splitcriteria Modifier and Type Interface Description interface
SplitCriterion
Interface for computing splitting criteria.Classes in moa.classifiers.core.splitcriteria that implement Configurable 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 Configurable in moa.classifiers.core.statisticaltests
Subinterfaces of Configurable in moa.classifiers.core.statisticaltests Modifier and Type Interface Description interface
StatisticalTest
This interface represents how to perform multivariate statistical tests.Classes in moa.classifiers.core.statisticaltests that implement Configurable 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 Configurable in moa.classifiers.deeplearning
Classes in moa.classifiers.deeplearning that implement Configurable Modifier and Type Class Description class
CAND
Continuously Adaptive Neural networks for Data streamsclass
MLP
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Uses of Configurable in moa.classifiers.drift
Classes in moa.classifiers.drift that implement Configurable 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 Configurable in moa.classifiers.functions
Classes in moa.classifiers.functions that implement Configurable 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 Configurable in moa.classifiers.lazy
Classes in moa.classifiers.lazy that implement Configurable 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 Configurable in moa.classifiers.meta
Classes in moa.classifiers.meta that implement Configurable 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 Configurable in moa.classifiers.meta.imbalanced
Classes in moa.classifiers.meta.imbalanced that implement Configurable 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 Configurable in moa.classifiers.multilabel
Classes in moa.classifiers.multilabel that implement Configurable 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 Configurable in moa.classifiers.multilabel.core.splitcriteria
Classes in moa.classifiers.multilabel.core.splitcriteria that implement Configurable Modifier and Type Class Description class
ICVarianceReduction
class
PCTWeightedICVarianceReduction
class
WeightedICVarianceReduction
Weighted intra cluster variance reduction split criterion -
Uses of Configurable in moa.classifiers.multilabel.meta
Classes in moa.classifiers.multilabel.meta that implement Configurable 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 Configurable in moa.classifiers.multilabel.trees
Classes in moa.classifiers.multilabel.trees that implement Configurable Modifier and Type Class Description class
ISOUPTree
iSOUPTree class for structured output prediction.class
ISOUPTreeRF
-
Uses of Configurable in moa.classifiers.multitarget
Classes in moa.classifiers.multitarget that implement Configurable Modifier and Type Class Description class
BasicMultiLabelClassifier
class
BasicMultiLabelLearner
Binary relevance Multilabel Classifierclass
BasicMultiTargetRegressor
Binary relevance Multi-Target Regressor -
Uses of Configurable in moa.classifiers.multitarget.functions
Classes in moa.classifiers.multitarget.functions that implement Configurable Modifier and Type Class Description class
MultiTargetNoChange
MultiTargetNoChange class regressor. -
Uses of Configurable in moa.classifiers.oneclass
Classes in moa.classifiers.oneclass that implement Configurable 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 Configurable in moa.classifiers.rules
Classes in moa.classifiers.rules that implement Configurable 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 Configurable in moa.classifiers.rules.core.anomalydetection
Subinterfaces of Configurable in moa.classifiers.rules.core.anomalydetection Modifier and Type Interface Description interface
AnomalyDetector
Anomaly Detector interface to implement methods that detects change.Classes in moa.classifiers.rules.core.anomalydetection that implement Configurable 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 Configurable in moa.classifiers.rules.core.anomalydetection.probabilityfunctions
Subinterfaces of Configurable in moa.classifiers.rules.core.anomalydetection.probabilityfunctions Modifier and Type Interface Description interface
ProbabilityFunction
Classes in moa.classifiers.rules.core.anomalydetection.probabilityfunctions that implement Configurable 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 Configurable in moa.classifiers.rules.core.attributeclassobservers
Classes in moa.classifiers.rules.core.attributeclassobservers that implement Configurable Modifier and Type Class Description class
FIMTDDNumericAttributeClassLimitObserver
-
Uses of Configurable in moa.classifiers.rules.core.changedetection
Classes in moa.classifiers.rules.core.changedetection that implement Configurable Modifier and Type Class Description class
NoChangeDetection
-
Uses of Configurable in moa.classifiers.rules.core.splitcriteria
Subinterfaces of Configurable in moa.classifiers.rules.core.splitcriteria Modifier and Type Interface Description interface
AMRulesSplitCriterion
Classes in moa.classifiers.rules.core.splitcriteria that implement Configurable Modifier and Type Class Description class
SDRSplitCriterionAMRules
class
SDRSplitCriterionAMRulesNode
class
VarianceRatioSplitCriterion
class
VRSplitCriterion
-
Uses of Configurable in moa.classifiers.rules.featureranking
Classes in moa.classifiers.rules.featureranking that implement Configurable 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 Configurable in moa.classifiers.rules.functions
Subinterfaces of Configurable in moa.classifiers.rules.functions Modifier and Type Interface Description interface
AMRulesLearner
interface
AMRulesRegressorFunction
Classes in moa.classifiers.rules.functions that implement Configurable Modifier and Type Class Description class
AdaptiveNodePredictor
class
FadingTargetMean
class
LowPassFilteredLearner
class
Perceptron
class
TargetMean
-
Uses of Configurable in moa.classifiers.rules.meta
Classes in moa.classifiers.rules.meta that implement Configurable Modifier and Type Class Description class
RandomAMRules
Random AMRules algoritgm that performs analogous procedure as the Random Forest Trees but with Rulesclass
RandomAMRulesOld
-
Uses of Configurable in moa.classifiers.rules.multilabel
Classes in moa.classifiers.rules.multilabel that implement Configurable 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
-
Uses of Configurable in moa.classifiers.rules.multilabel.attributeclassobservers
Subinterfaces of Configurable in moa.classifiers.rules.multilabel.attributeclassobservers Modifier and Type Interface Description interface
AttributeStatisticsObserver
Interface for observing the statistics for an attribute.interface
NominalStatisticsObserver
interface
NumericStatisticsObserver
Classes in moa.classifiers.rules.multilabel.attributeclassobservers that implement Configurable 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 variables -
Uses of Configurable in moa.classifiers.rules.multilabel.core
Classes in moa.classifiers.rules.multilabel.core that implement Configurable Modifier and Type Class Description 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
-
Uses of Configurable in moa.classifiers.rules.multilabel.core.splitcriteria
Subinterfaces of Configurable in moa.classifiers.rules.multilabel.core.splitcriteria Modifier and Type Interface Description interface
MultiLabelSplitCriterion
Classes in moa.classifiers.rules.multilabel.core.splitcriteria that implement Configurable Modifier and Type Class Description class
MultilabelInformationGain
Multi-label Information Gain.class
MultiTargetVarianceRatio
-
Uses of Configurable in moa.classifiers.rules.multilabel.errormeasurers
Subinterfaces of Configurable in moa.classifiers.rules.multilabel.errormeasurers Modifier and Type Interface Description interface
MultiLabelErrorMeasurer
interface
MultiTargetErrorMeasurer
Classes in moa.classifiers.rules.multilabel.errormeasurers that implement Configurable 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 Configurable in moa.classifiers.rules.multilabel.functions
Classes in moa.classifiers.rules.multilabel.functions that implement Configurable 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 Configurable in moa.classifiers.rules.multilabel.inputselectors
Subinterfaces of Configurable in moa.classifiers.rules.multilabel.inputselectors Modifier and Type Interface Description interface
InputAttributesSelector
Classes in moa.classifiers.rules.multilabel.inputselectors that implement Configurable Modifier and Type Class Description class
MeritThreshold
Input selection algorithm based on Merit thresholdclass
SelectAllInputs
Does not selects inputs -
Uses of Configurable in moa.classifiers.rules.multilabel.meta
Classes in moa.classifiers.rules.multilabel.meta that implement Configurable Modifier and Type Class Description class
MultiLabelRandomAMRules
-
Uses of Configurable in moa.classifiers.rules.multilabel.outputselectors
Subinterfaces of Configurable in moa.classifiers.rules.multilabel.outputselectors Modifier and Type Interface Description interface
OutputAttributesSelector
Classes in moa.classifiers.rules.multilabel.outputselectors that implement Configurable 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 Configurable in moa.classifiers.trees
Classes in moa.classifiers.trees that implement Configurable 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 Configurable in moa.classifiers.trees.iadem
Subinterfaces of Configurable in moa.classifiers.trees.iadem Modifier and Type Interface Description interface
IademNumericAttributeObserver
Classes in moa.classifiers.trees.iadem that implement Configurable Modifier and Type Class Description class
Iadem2
class
Iadem3
class
Iadem3Subtree
class
IademGaussianNumericAttributeClassObserver
class
IademGreenwaldKhannaNumericAttributeClassObserver
class
IademVFMLNumericAttributeClassObserver
-
Uses of Configurable in moa.clusterers
Subinterfaces of Configurable in moa.clusterers Modifier and Type Interface Description interface
Clusterer
Classes in moa.clusterers that implement Configurable Modifier and Type Class Description class
AbstractClusterer
class
ClusterGenerator
class
CobWeb
Class implementing the Cobweb and Classit clustering algorithms.class
WekaClusteringAlgorithm
-
Uses of Configurable in moa.clusterers.clustream
Classes in moa.clusterers.clustream that implement Configurable Modifier and Type Class Description class
Clustream
Citation: CluStream: Charu C.class
WithKmeans
-
Uses of Configurable in moa.clusterers.clustree
Classes in moa.clusterers.clustree that implement Configurable Modifier and Type Class Description class
ClusTree
Citation: ClusTree: Philipp Kranen, Ira Assent, Corinna Baldauf, Thomas Seidl: The ClusTree: indexing micro-clusters for anytime stream mining. -
Uses of Configurable in moa.clusterers.denstream
Classes in moa.clusterers.denstream that implement Configurable Modifier and Type Class Description class
WithDBSCAN
-
Uses of Configurable in moa.clusterers.dstream
Classes in moa.clusterers.dstream that implement Configurable Modifier and Type Class Description class
Dstream
Citation: Y. -
Uses of Configurable in moa.clusterers.kmeanspm
Classes in moa.clusterers.kmeanspm that implement Configurable Modifier and Type Class Description class
BICO
A instance of this class provides the BICO clustering algorithm. -
Uses of Configurable in moa.clusterers.meta
Classes in moa.clusterers.meta that implement Configurable Modifier and Type Class Description class
ConfStream
class
EnsembleClustererAbstract
-
Uses of Configurable in moa.clusterers.outliers
Classes in moa.clusterers.outliers that implement Configurable Modifier and Type Class Description class
MyBaseOutlierDetector
-
Uses of Configurable in moa.clusterers.outliers.AbstractC
Classes in moa.clusterers.outliers.AbstractC that implement Configurable Modifier and Type Class Description class
AbstractC
class
AbstractCBase
-
Uses of Configurable in moa.clusterers.outliers.Angiulli
Classes in moa.clusterers.outliers.Angiulli that implement Configurable Modifier and Type Class Description class
ApproxSTORM
class
ExactSTORM
class
STORMBase
-
Uses of Configurable in moa.clusterers.outliers.AnyOut
Classes in moa.clusterers.outliers.AnyOut that implement Configurable Modifier and Type Class Description class
AnyOut
class
AnyOutCore
-
Uses of Configurable in moa.clusterers.outliers.MCOD
Classes in moa.clusterers.outliers.MCOD that implement Configurable Modifier and Type Class Description class
MCOD
class
MCODBase
-
Uses of Configurable in moa.clusterers.outliers.SimpleCOD
Classes in moa.clusterers.outliers.SimpleCOD that implement Configurable Modifier and Type Class Description class
SimpleCOD
class
SimpleCODBase
-
Uses of Configurable in moa.clusterers.streamkm
Classes in moa.clusterers.streamkm that implement Configurable Modifier and Type Class Description class
StreamKM
-
Uses of Configurable in moa.evaluation
Classes in moa.evaluation that implement Configurable Modifier and Type Class Description class
AdwinClassificationPerformanceEvaluator
Classification evaluator that updates evaluation results using an adaptive sliding window.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
EWMAClassificationPerformanceEvaluator
Classification evaluator that updates evaluation results using an Exponential Weighted Moving Average.class
FadingFactorClassificationPerformanceEvaluator
Classification evaluator that updates evaluation results using a fading factor.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
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 Configurable in moa.gui.experimentertab.tasks
Classes in moa.gui.experimentertab.tasks that implement Configurable 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 Configurable in moa.learners
Subinterfaces of Configurable in moa.learners Modifier and Type Interface Description interface
Learner<E extends Example>
Learner interface for incremental learning models.interface
LearnerSemiSupervised<E extends Example>
Classes in moa.learners that implement Configurable Modifier and Type Class Description class
ChangeDetectorLearner
Class for detecting concept drift and to be used as a learner. -
Uses of Configurable in moa.learners.featureanalysis
Subinterfaces of Configurable in moa.learners.featureanalysis Modifier and Type Interface Description interface
FeatureImportanceClassifier
Feature Importance ClassifierClasses in moa.learners.featureanalysis that implement Configurable 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 Configurable in moa.options
Subinterfaces of Configurable in moa.options Modifier and Type Interface Description interface
OptionHandler
Interface representing an object that handles options or parameters.Classes in moa.options that implement Configurable Modifier and Type Class Description class
AbstractOptionHandler
Abstract Option Handler. -
Uses of Configurable in moa.recommender.data
Classes in moa.recommender.data that implement Configurable Modifier and Type Class Description class
MemRecommenderData
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Uses of Configurable in moa.recommender.dataset.impl
Classes in moa.recommender.dataset.impl that implement Configurable Modifier and Type Class Description class
FlixsterDataset
class
JesterDataset
class
MovielensDataset
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Uses of Configurable in moa.recommender.predictor
Classes in moa.recommender.predictor that implement Configurable 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 Configurable in moa.streams
Classes in moa.streams that implement Configurable Modifier and Type Class Description class
ArffFileStream
Stream reader of ARFF files.class
BootstrappedStream
Bootstrapped Streamclass
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 Configurable in moa.streams.clustering
Classes in moa.streams.clustering that implement Configurable Modifier and Type Class Description class
ClusteringStream
class
FileStream
class
RandomRBFGeneratorEvents
class
SimpleCSVStream
Provides a simple input stream for csv files. -
Uses of Configurable in moa.streams.filters
Classes in moa.streams.filters that implement Configurable 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 Configurable in moa.streams.generators
Classes in moa.streams.generators that implement Configurable 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 Configurable in moa.streams.generators.cd
Classes in moa.streams.generators.cd that implement Configurable Modifier and Type Class Description class
AbruptChangeGenerator
class
AbstractConceptDriftGenerator
class
GradualChangeGenerator
class
NoChangeGenerator
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Uses of Configurable in moa.streams.generators.multilabel
Classes in moa.streams.generators.multilabel that implement Configurable Modifier and Type Class Description class
MetaMultilabelGenerator
Stream generator for multilabel data.class
MultilabelArffFileStream
Stream reader for ARFF files of multilabel data. -
Uses of Configurable in moa.tasks
Classes in moa.tasks that implement Configurable 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
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 Configurable in moa.tasks.meta
Classes in moa.tasks.meta that implement Configurable 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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