A fuzzy associative classification system with genetic rule selection for high-dimensional problems

The learning of Fuzzy Rule-Based Classification Systems for High-Dimensional problems suffers from exponential growth of the fuzzy rule search space when the number of patterns and/or variables becomes high. In this work, we propose a fuzzy association rule-based classification method with genetic rule selection for high-dimensional problems to obtain an accurate and compact fuzzy rule-based classifier with low computational cost. The results obtained from the comparison with other two genetic fuzzy systems over nine real-world datasets with different characteristics show the effectiveness of the proposed approach.