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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 1/2018

31.01.2015 | Original Article

A novel online ensemble approach to handle concept drifting data streams: diversified dynamic weighted majority

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 1/2018

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Abstract

We present an online ensemble approach, diversified dynamic weighted majority (DDWM) to classify new data instances which have varying conceptual distributions. Our approach maintains two sets of weighted ensembles that differentiate in their level of diversity. An expert in either of the ensembles is updated or removed as per its classification accuracy and a new expert is added based on the final global prediction of the algorithm and the global prediction of the ensemble for any data instance. Experimental evaluation using various artificial and real-world datasets proves that DDWM provides very high accuracy in classifying new data instances, irrespective of size of dataset, type of drift or presence of noise. We compare DDWM with the other learners in terms of new performance metrics such as kappa statistic, model cost, and the evaluation time and memory requirements. Our approach proved to be highly resource effective achieving very high accuracies even in a resource constrained environment.
Vorheriger Artikel NBWELM: naive Bayesian based weighted extreme learning machine
Nächster Artikel The effect of online reviews on e-tailers’ pricing in a dual-channel market with competition

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Metadaten
Titel
A novel online ensemble approach to handle concept drifting data streams: diversified dynamic weighted majority
Publikationsdatum
31.01.2015
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 1/2018
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-015-0333-x

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