Feature Selection and Rule Generation Integrated Learning for Takagi-Sugeno-Kang Fuzzy System and its Application in Medical Data Classification

Feature Selection and Rule Generation Integrated Learning for Takagi-Sugeno-Kang Fuzzy System and its Application in Medical Data Classification

Blog Article

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) in this paper.FSRG-IL-TSK read more 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 sophie allport zebra 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 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.