The rule-based fuzzy systems have successfully applied for numerous medical data classification problems.However, structuring the concise and interpretable fuzzy rules with good classification performance is still a big challenge.To address this issue, a novel feature selection and rule generation integrated learning for Takagi-Sugeno-Kang fuzzy system (called FSRG-IL-TSK) read more in this paper.FSRG-IL-TSK represents feature selection, structure identification and parameter learning into a Bayesian model, and uses the sequential importance resampling (SIR) algorithm to obtain the optimal parameters simultaneously, including the optimal features for each fuzzy rule, number of rules, and antecedent/consequent parameter of rules.Due to an integrated learning mechanism, it can select a small set of useful read more features and obtain a small number of rules.
The effectiveness and advantages of FSRG-IL-TSK are validated experimentally on real-world medical data classification tasks.