A NEW HEURISTIC APPROACH FOR TRAINING DATA REDUCTION AND A GENETIC LEARNING METHOD FOR ACHEIVING COMPACT FUZZY RULE-BASED SYSTEMS

This paper is to introduce a heuristic method for selecting a subset of instances from the training data set in high dimensional problems. This subset is called the representative training data set (RTR). A proposed genetic algorithm (GA) is used to learn a compact fuzzy rule-based system (FRBS) with the instances of RTR. RTR size is rather smaller than the initial training data set, thus time cost for learning FRBS decreases significantly. Therein the number of fuzzy rules is reduced. The smaller size of the rule base is closely related to the interpretability of the FRBS. As a result, the final FBRS gets a suitable and acceptable balance between interpretability and accuracy.