FERHD: A feasible approach for extracting fuzzy classification rules from high-dimensional data

Extracting comprehensive rules from high-dimensional data is a serious challenge in designing fuzzy classifiers. Among several methods for generating rules from data, mostly often work efficiently for low dimensions. Indeed, when dimensions go up, the number of generated rules becomes unmanageable. In this paper, a feasible approach for extracting rules from high-dimensional data (FERHD) is proposed. Unlike top-down methods which generate some general fuzzy rules and then try to make them specific, our method works in a bottom-up manner. It first generates all manageable specific rules and then tries to generalize them. In this regard, after partitioning the problem space into some fuzzy grids, FERHD generates rules for these partitions if there are at least one training pattern in their decision subspace. Thus, FERHD is scalable since it generates at most m rules for a dataset of size m. Also, it decides on suitable number of fuzzy sets to be used for attributes via the generalization process which in turn produces a small-size rule base. To justify the scalability of FERHD on high-dimensional datasets, it is used to extract rules from some benchmark datasets. In comparing with some related methods, the accuracy and interpretability of the designed classifiers are acceptable.