Hybrid Methods for Extracting Fuzzy Classification Rules from Mixed Data

Classification problems with different types of variables can be difficult to solve. In this paper, we propose hybrid methods for extracting fuzzy classification rules from mixed data. A new frequency method was proposed to form the membership degree of fuzzy nominal values and compared with two other methods. The performance of the proposed methods is confirmed by a series of experiments on real-world mixed data sets. Empirical evaluations confirm the exceptional performance demonstrated by the proposed frequents method for processing mixed data.