hGA: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems

This paper provides a corrected formulation to the mixed integer programming model proposed by Aydogan et al. (2012) [1]. They proposed a genetic algorithm to learn fuzzy rules for a fuzzy rule-based classification system and developed a Mixed Integer Programming model (MIP) to prune the generated rules by selecting the best set of rules to maximize predictive accuracy. However, their proposed MIP formulation contains errors, which are described in this technical note. We develop corrections eywords: uzzy rule-based classification systems nteger programming enetic algorithms enetic fuzzy systems lassification and improvements to the original formulation and test it with non-parametric statistical tests on the same data sets used to evaluate the original model. The statistical analysis shows that the results of the correction formulation are significantly different from the original model. © 2015 Elsevier B.V. All rights reserved.