Simple fuzzy rule-based classification systems perform well on commonly used real-world data sets

Real-world pattern classification problems usually involve many attributes. Thus, it is often claimed that fuzzy rule-based systems with grid-type fuzzy partitions are not applicable to such pattern classification problems due to the exponential increase of the number of fuzzy if-then rules (i.e., the curse of dimensionality). When we use K antecedent fuzzy sets for each attribute of an n-dimensional pattern classification problem, the total number of possible fuzzy if-then rules is K-n, which is intractably huge for a large value of n. Thus we can not directly apply grid-type fuzzy partitions to high-dimensional pattern classification problems. If a few attributes can be selected from a large number of attributes for a high-dimensional pattern classification problem, we can use a grid-type fuzzy partition. The point is whether grid-type fuzzy partitions based on a few attributes have high classification ability or not. The aim of this paper is to examine the performance of such fuzzy partitions by computer simulations on real-world pattern classification problems with many attributes. Simulation results clearly show that a few attributes have high generalization ability for some real-world pattern classification problems.