Welcome to the InfoSci Platform
Reference Hub6
A New MapReduce Approach with Dynamic Fuzzy Inference for Big Data Classification Problems

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

Shangzhu Jin, Jun Peng, Dong Xie
Copyright: © 2018 |Volume: 12 |Issue: 3 |Pages: 15
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781522543046|DOI: 10.4018/IJCINI.2018070103
Cite Article Cite Article

MLA

Jin, Shangzhu, et al. "A New MapReduce Approach with Dynamic Fuzzy Inference for Big Data Classification Problems." IJCINI vol.12, no.3 2018: pp.40-54. http://doi.org/10.4018/IJCINI.2018070103

APA

Jin, S., Peng, J., & Xie, D. (2018). A New MapReduce Approach with Dynamic Fuzzy Inference for Big Data Classification Problems. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 12(3), 40-54. http://doi.org/10.4018/IJCINI.2018070103

Chicago

Jin, Shangzhu, Jun Peng, and Dong Xie. "A New MapReduce Approach with Dynamic Fuzzy Inference for Big Data Classification Problems," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 12, no.3: 40-54. http://doi.org/10.4018/IJCINI.2018070103

Export Reference

Mendeley
Favorite Full-Issue Download

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.