A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning

Abstract—The inductive learning of fuzzy rule-based classifi-cation systems suffers from exponential growth of the fuzzy rule search space when the number of patterns and/or variables be-comes high. This growth makes the learning process more difficult and, in most cases, it leads to problems of scalability (in terms of the time and memory consumed) and/or complexity (with respect to the number of rules obtained and the number of variables included in each rule). In this paper, we propose a fuzzy association rule-based classification method for high-dimensional problems, which is based on three stages to obtain an accurate and compact fuzzy rule-based classifier with a low computational cost. This method limits the order of the associations in the association rule extrac-tion and considers the use of subgroup discovery, which is based on an improved weighted relative accuracy measure to preselect the most interesting rules before a genetic postprocessing process for rule selection and parameter tuning. The results that are ob-tained more than 26 real-world datasets of different sizes and with different numbers of variables demonstrate the effectiveness of the proposed approach. Index Terms—Associative classification, classification, data min-ing, fuzzy association rules, genetic algorithms (GAs), genetic fuzz