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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 3/2019

02.11.2017 | Original Article

A two ensemble system to handle concept drifting data streams: recurring dynamic weighted majority

verfasst von: Parneeta Sidhu, M. P. S. Bhatia

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 3/2019

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Abstract

We present an ensemble system, recurring dynamic weighted majority (RDWM) that maintains two ensembles of experts, so as to accurately handle drifting concepts mainly recurrent drifts. The primary online ensemble represents the present concepts and the secondary ensemble represents the old concepts since the beginning of learning. An effective pruning methodology helps to remove redundant and old classifiers, which may have otherwise caused interference in learning the new concepts. Experimental evaluation using datasets proves that RDWM achieves very high generalization accuracy, irrespective of the speed or severity of drift; or presence of noise in the dataset.
Vorheriger Artikel An effective Bayesian network parameters learning algorithm for autonomous mission decision-making under scarce data
Nächster Artikel Global exponential synchronization of delayed fuzzy cellular neural networks with discontinuous activations

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Metadaten
Titel
A two ensemble system to handle concept drifting data streams: recurring dynamic weighted majority
verfasst von
Parneeta Sidhu
M. P. S. Bhatia
Publikationsdatum
02.11.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 3/2019
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-017-0738-9

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