Reducing the Complexity of Genetic Fuzzy Classifiers in Highly-Dimensional Classification Problems

TitleReducing the Complexity of Genetic Fuzzy Classifiers in Highly-Dimensional Classification Problems
Publication TypeJournal Article
Year of Publication2012
AuthorsGalidaki, GN, Ioannis Z. Gitas, Theocharis, JB
JournalInternational Journal of Computational Intelligence Systems
Pagination254 - 275
Date PublishedJan-04-2012
KeywordsGenetic fuzzy rule-based classification systems (GFRBCS), genetic tuning, highly-dimensional classification problems, hyperspectral image classification, local feature selection

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.



Short TitleInternational Journal of Computational Intelligence Systems