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A New MapReduce Approach with Dynamic Fuzzy Inference for Big Data Classification Problems

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  • Shangzhu Jin

    (College of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, China)

  • Jun Peng

    (College of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, China)

  • Dong Xie

    (College of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, China)

Abstract

Currently, big data and its applications have become one of the emergent topics. In practice, MapReduce framework and its different extensions are the most popular approaches for big data. Fuzzy system based models stand out for many applications. However, when a given observation has no overlap with antecedent values, no rule can be invoked in classical fuzzy inference can also appear in big data environment, and therefore no consequence can be derived. Fortunately, fuzzy rule interpolation techniques can support inference in such cases. Combining traditional fuzzy reasoning technique and fuzzy interpolation method may promote the accuracy of inference conclusion. Therefore, in this article, an initial investigation into the framework of MapReduce with dynamic fuzzy inference/interpolation for big data applications (BigData-DFRI) is reported. The results of an experimental investigation of this method are represented, demonstrating the potential and efficacy of the proposed approach.

Suggested Citation

  • Shangzhu Jin & Jun Peng & Dong Xie, 2018. "A New MapReduce Approach with Dynamic Fuzzy Inference for Big Data Classification Problems," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 12(3), pages 40-54, July.
    • Handle: RePEc:igg:jcini0:v:12:y:2018:i:3:p:40-54
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