title:
 
An Efficient Inductive Genetic Learning Algorithm for Fuzzy Relational Rules
publication:
 
IJCIS
volume-issue:   5 - 2
pages:   212 - 230
ISSN:
  1875-6883
DOI:
  doi:10.2991/10.1080/18756891.2012.685265 (how to use a DOI)
author(s):
 
Antonio González, Raúl Pérez, Yoel Caises, Enrique Leyva
publication date:
 
April 2012
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:
 
© The authors.
This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/
full text: