title:
 
Genetic Learning of Fuzzy Parameters in Predictive and Decision Support Modelling
publication:
 
IJCIS
volume-issue:   5 - 2
pages:   387 - 402
ISSN:
  1875-6883
DOI:
  doi:10.2991/10.1080/18756891.2012.685328 (how to use a DOI)
author(s):
 
Àngela Nebot, Francisco Mugica, Félix Castro, Jesús Acosta
publication date:
 
April 2012
keywords:
 
Genetic fuzzy systems, fuzzy inductive reasoning, predictive models, decision support models, e-learning
abstract:
 
In this research a genetic fuzzy system (GFS) is proposed that performs discretization parameter learning in the context of the Fuzzy Inductive Reasoning (FIR) methodology and the Linguistic Rule FIR (LR-FIR) algorithm. The main goal of the GFS is to take advantage of the potentialities of GAs to learn the fuzzification parameters of the FIR and LR-FIR approaches in order to obtain reliable and useful predictive (FIR) models and decision support (LR-FIR) models. The GFS is evaluated in an e-learning context.
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: