Stochastic Configured Fuzzy System Modeling based on Information Granulation and Association Analysis

In this paper, a stochastic configured fuzzy system with semantic interpretability is proposed and used for classification. Firstly, transfer entropy is adopted to conduct causal analysis on modeling variables. To reduce computational complexity and achieve effective screening of data variables, the information granule is designed to preprocess the data. Secondly, a research is carried out on the fuzzy modeling method based on the idea of stochastic configuration. Besides, an improved fuzzy system evaluation index for model parameter learning and system structure optimization is proposed. The improved index introduces the axiomatic fuzzy set theory to evaluate the information metric of modeling results, which increases the semantic interpretability of the fuzzy system. Finally, the proposed method is applied in some medical data sets. The experimental results show that it offers better performance in solving the classification problem.