Sugeno fuzzy integral for finding fuzzy if–then classification rules

It is known that data mining techniques can be used to discover useful information by exploring and analyzing data. For classification problems, this paper uses the Sugeno fuzzy integral to determine the degrees of importance for individual fuzzy grids that are generated by partitioning each data attribute with various linguistic values; then, fuzzy if–then classification rules are discovered from those fuzzy grids whose degree of importance is larger than or equal to a user-specified minimum threshold. In the proposed method, since it is difficult for users to specify partition numbers in quantitative attributes, the degree of importance for each training pattern, and user-specified minimum thresholds, the aforementioned parameter specifications are determined by evolutionary computations of genetic algorithms (GA). For examining the generalization ability, the simulation results from the iris data and the appendicitis data show that the proposed method performs well in comparison with many well-known classification methods.