Why Linguistic Fuzzy Rule Based Classification Systems perform well in Big Data Applications?

DOI

10.2991/ijcis.10.1.80How to use a DOI?

Keywords

Big Data; Fuzzy Rule Based Classification Systems; Interpretability; MapReduce; Hadoop

Abstract

The significance of addressing Big Data applications is beyond all doubt. The current ability of extracting interesting knowledge from large volumes of information provides great advantages to both corporations and academia. Therefore, researchers and practitioners must deal with the problem of scalability so that Machine Learning and Data Mining algorithms can address Big Data properly. With this end, the MapReduce programming framework is by far the most widely used mechanism to implement fault-tolerant distributed applications. This novel framework implies the design of a divide-and-conquer mechanism in which local models are learned separately in one stage (Map tasks) whereas a second stage (Reduce) is devoted to aggregate all sub-models into a single solution. In this paper, we focus on the analysis of the behavior of Linguistic Fuzzy Rule Based Classification Systems when embedded into a MapReduce working procedure. By retrieving different information regarding the rules learned throughout the MapReduce process, we will be able to identify some of the capabilities of this particular paradigm that allowed them to provide a good performance when addressing Big Data problems. In summary, we will show that linguistic fuzzy classifiers are a robust approach in case of scalability requirements.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

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

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TY  - JOUR
AU  - Alberto Fernández
AU  - Abdulrahman Altalhi
AU  - Saleh Alshomrani
AU  - Francisco Herrera
PY  - 2017
DA  - 2017/08/30
TI  - Why Linguistic Fuzzy Rule Based Classification Systems perform well in Big Data Applications?
JO  - International Journal of Computational Intelligence Systems
SP  - 1211
EP  - 1225
VL  - 10
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.10.1.80
DO  - 10.2991/ijcis.10.1.80
ID  - Fernández2017
ER  -