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Efficient mining of high-speed uncertain data streams

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Abstract

Currently available algorithms for data streams classification are mostly designed to deal with precise and complete data. However, data in many real-life applications is naturally uncertain due to inherent instrument inaccuracy, wireless transmission error, and so on. We propose UELM-MapReduce, a parallel ensemble classifier based on Extreme Learning Machine (ELM) and MapReduce for handling uncertain data streams. We train an efficient parallel ELM-based ensemble classifier from sequential training chunks of the uncertain data streams. The weight of each base classifier in the ensemble is adjusted according to its mean square error on the up-to-date test chunk, and the classifier with the lowest accuracy is replaced. UELM-MapReduce can classify uncertain data streams with both efficiency and accuracy while effectively handling concept drift. Experimental results demonstrate that UELM-MapReduce has better performance than other methods in prediction accuracy and computational efficiency.

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Acknowledgments

This work is supported by the National Nature Science Foundation of China (No. 61173029, 6122182). It was performed while the first author was a Visiting Scholar at Brigham Young University.

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Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, Shenyang, China

    Donghong Han & Shuoru Li

  2. Key Laboratory of Medical Image Computing (NEU), Ministry of Education, Shenyang, China

    Donghong Han

  3. Department of Computer Science, Brigham Young University, Provo, UT, USA

    Christophe Giraud-Carrier

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  1. Donghong Han
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  2. Christophe Giraud-Carrier
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  3. Shuoru Li
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Correspondence to Christophe Giraud-Carrier.

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Han, D., Giraud-Carrier, C. & Li, S. Efficient mining of high-speed uncertain data streams. Appl Intell 43, 773–785 (2015). https://doi.org/10.1007/s10489-015-0675-9

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  • DOI: https://doi.org/10.1007/s10489-015-0675-9

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