Reducing the Complexity of Genetic Fuzzy Classifiers in Highly-Dimensional Classification Problems
volume-issue:   5 - 2
pages:   254 - 275
  doi:10.2991/10.1080/18756891.2012.685290 (how to use a DOI)
DimitrisG. Stavrakoudis, GeorgiaN. Galidaki, IoannisZ. Gitas, JohnB. Theocharis
publication date:
April 2012
Genetic fuzzy rule-based classification systems (GFRBCS), local feature selection, genetic tuning, hyperspectral image classification, highly-dimensional classification problems
This paper introduces the Fast Iterative Rule-based Linguistic Classifier (FaIRLiC), a Genetic Fuzzy Rule-Based Classification System (GFRBCS) which targets at reducing the structural complexity of the resulting rule base, as well as its learning algorithm's computational requirements, especially when dealing with high-dimensional feature spaces. The proposed methodology follows the principles of the iterative rule learning (IRL) approach, whereby a rule extraction algorithm (REA) is invoked in an iterative fashion, producing one fuzzy rule at a time. The REA is performed in two successive steps: the first one selects the relevant features of the currently extracted rule, whereas the second one decides the antecedent part of the fuzzy rule, using the previously selected subset of features. The performance of the classifier is finally optimized through a genetic tuning post-processing stage. Comparative results in a hyperspectral remote sensing classification as well as in 12 real-world classification datasets indicate the effectiveness of the proposed methodology in generating high-performing and compact fuzzy rule-based classifiers, even for very high-dimensional feature spaces.
© 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: