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

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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.

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Authors and Affiliations

  1. Division of CoE, Netaji Subhas Institute of Technology, Sec-3, Dwarka, New Delhi, 110078, India

    Parneeta Sidhu & M. P. S. Bhatia

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  1. Parneeta Sidhu
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  2. M. P. S. Bhatia
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Correspondence to Parneeta Sidhu.

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Sidhu, P., Bhatia, M.P.S. A two ensemble system to handle concept drifting data streams: recurring dynamic weighted majority. Int. J. Mach. Learn. & Cyber. 10, 563–578 (2019). https://doi.org/10.1007/s13042-017-0738-9

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  • DOI: https://doi.org/10.1007/s13042-017-0738-9

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