An Efficient Inductive Genetic Learning Algorithm for Fuzzy Relational Rules

DOI

10.1080/18756891.2012.685265How to use a DOI?

Keywords

Genetic fuzzy learning, fuzzy rules, fuzzy relational rules, classification

Abstract

Fuzzy modelling research has traditionally focused on certain types of fuzzy rules. However, the use of alternative rule models could improve the ability of fuzzy systems to represent a specific problem. In this proposal, an extended fuzzy rule model, that can include relations between variables in the antecedent of rules is presented. Furthermore, a learning algorithm based on the iterative genetic approach which is able to represent the knowledge using this model is proposed as well. On the other hand, potential relations among initial variables imply an exponential growth in the feasible rule search space. Consequently, two filters for detecting relevant potential relations are added to the learning algorithm. These filters allows to decrease the search space complexity and increase the algorithm efficiency. Finally, we also present an experimental study to demonstrate the benefits of using fuzzy relational rules.

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  - Antonio González
AU  - Raúl Pérez
AU  - Yoel Caises
AU  - Enrique Leyva
PY  - 2012
DA  - 2012/04/01
TI  - An Efficient Inductive Genetic Learning Algorithm for Fuzzy Relational Rules
JO  - International Journal of Computational Intelligence Systems
SP  - 212
EP  - 230
VL  - 5
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2012.685265
DO  - 10.1080/18756891.2012.685265
ID  - González2012
ER  -