title:
 
A Study on the Use of Multiobjective Genetic Algorithms for Classifier Selection in FURIA-based Fuzzy Multiclassifiers
publication:
 
IJCIS
volume-issue:   5 - 2
pages:   231 - 253
ISSN:
  1875-6883
DOI:
  doi:10.1080/18756891.2012.685272 (how to use a DOI)
author(s):
 
Krzysztof TrawiÅ„ski, Oscar Cordón, Arnaud Quirin
publication date:
 
April 2012
keywords:
 
Fuzzy rule-based multiclassification systems, bagging, FURIA, genetic selection of individual classifiers, diversity measures, evolutionary multiobjective optimization, NSGA-II
abstract:
 
In a preceding contribution, we conducted a study considering a fuzzy multiclassifier system (MCS) design framework based on Fuzzy Unordered Rule Induction Algorithm (FURIA). It served as the fuzzy rule classification learning algorithm to derive the component classifiers considering bagging and feature selection. In this work, we integrate this approach under the overproduce-and-choose strategy. A state-of-the-art evolutionary multiobjective algorithm, namely NSGA-II, is used to provide a component classifier selection and improve FURIA-based fuzzy MCS. We propose five different fitness functions based on three different optimization criteria, accuracy, complexity, and diversity. Twenty UCI high dimensional datasets were considered in order to conduct the experiments. A combination between accuracy and diversity criteria provided very promising results, becoming competitive with classical MCS learning methods.
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/
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