A MapReduce Approach to Address Big Data Classification Problems Based on the Fusion of Linguistic Fuzzy Rules

DOI

10.1080/18756891.2015.1017377How to use a DOI?

Keywords

Fuzzy rule based classification systems, Big data, MapReduce, Hadoop, Rules fusion

Abstract

The big data term is used to describe the exponential data growth that has recently occurred and represents an immense challenge for traditional learning techniques. To deal with big data classification problems we propose the Chi-FRBCS-BigData algorithm, a linguistic fuzzy rule-based classification system that uses the MapReduce framework to learn and fuse rule bases. It has been developed in two versions with different fusion processes. An experimental study is carried out and the results obtained show that the proposal is able to handle these problems providing competitive results.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Sara del Río
AU  - Victoria López
AU  - José Manuel Benítez
AU  - Francisco Herrera
PY  - 2015
DA  - 2015/06/01
TI  - A MapReduce Approach to Address Big Data Classification Problems Based on the Fusion of Linguistic Fuzzy Rules
JO  - International Journal of Computational Intelligence Systems
SP  - 422
EP  - 437
VL  - 8
IS  - 3
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2015.1017377
DO  - 10.1080/18756891.2015.1017377
ID  - delRío2015
ER  -