Package moa.classifiers.meta

Class Summary

Class	Description
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.
AccuracyWeightedEnsemble	The Accuracy Weighted Ensemble classifier as proposed by Wang et al.
ADACC	Anticipative and Dynamic Adaptation to Concept Changes.
AdaptiveRandomForest	Adaptive Random Forest
AdaptiveRandomForestRegressor	Implementation of AdaptiveRandomForestRegressor, an extension of AdaptiveRandomForest for classification.
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.
BOLE
DACC	Dynamic Adaptation to Concept Changes.
DynamicWeightedMajority	Dynamic weighted majority algorithm.
HeterogeneousEnsembleAbstract	BLAST (Best Last) for Heterogeneous Ensembles Abstract Base Class
HeterogeneousEnsembleBlast	BLAST (Best Last) for Heterogeneous Ensembles implemented with Fading Factors
HeterogeneousEnsembleBlastFadingFactors	BLAST (Best Last) for Heterogeneous Ensembles implemented with Fading Factors
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.
LeveragingBag	Leveraging Bagging for evolving data streams using ADWIN.
LimAttClassifier	Ensemble Combining Restricted Hoeffding Trees using Stacking.
OCBoost	Online Coordinate boosting for two classes evolving data streams.
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.
OnlineSmoothBoost	Incremental on-line boosting with Theoretical Justifications of Shang-Tse Chen, Hsuan-Tien Lin and Chi-Jen Lu.
OzaBag	Incremental on-line bagging of Oza and Russell.
OzaBagAdwin	Bagging for evolving data streams using ADWIN.
OzaBagASHT	Bagging using trees of different size.
OzaBoost	Incremental on-line boosting of Oza and Russell.
OzaBoostAdwin	Boosting for evolving data streams using ADWIN.
PairedLearners	Creates two classifiers: a stable and a reactive.
RandomRules
RCD	Creates a set of classifiers, each one representing a different context.
TemporallyAugmentedClassifier	Include labels of previous instances into the training data
WeightedMajorityAlgorithm	Weighted majority algorithm for data streams.
WEKAClassifier	Class for using a classifier from WEKA.