DOAJ Open Access 2019

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

Xiaoqing Gu Cong Zhang Tongguang Ni

Abstrak

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 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 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.

Topik & Kata Kunci

Electrical engineering. Electronics. Nuclear engineering

Penulis (3)

Xiaoqing Gu

Cong Zhang

Tongguang Ni

Format Sitasi

APA MLA BibTeX

Gu, X., Zhang, C., Ni, T. (2019). Feature Selection and Rule Generation Integrated Learning for Takagi-Sugeno-Kang Fuzzy System and its Application in Medical Data Classification. https://doi.org/10.1109/ACCESS.2019.2954707

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Informasi Jurnal

Tahun Terbit: 2019
Sumber Database: DOAJ
DOI: 10.1109/ACCESS.2019.2954707
Akses: Open Access ✓