Feature Selection and Rule Generation Integrated Learning for Takagi-Sugeno-Kang Fuzzy System and its Application in Medical Data Classification
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8907879/ |
| _version_ | 1831560745724149760 |
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| author | Xiaoqing Gu Cong Zhang Tongguang Ni |
| author_facet | Xiaoqing Gu Cong Zhang Tongguang Ni |
| author_sort | Xiaoqing Gu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T05:52:08Z |
| format | Article |
| id | doaj.art-000e764ff13a4d8a9f43655d8fd34cc2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:52:08Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-000e764ff13a4d8a9f43655d8fd34cc22022-12-21T22:01:08ZengIEEEIEEE Access2169-35362019-01-01716902916903710.1109/ACCESS.2019.29547078907879Feature Selection and Rule Generation Integrated Learning for Takagi-Sugeno-Kang Fuzzy System and its Application in Medical Data ClassificationXiaoqing Gu0https://orcid.org/0000-0001-9942-0651Cong Zhang1https://orcid.org/0000-0001-6845-8761Tongguang Ni2https://orcid.org/0000-0002-0354-5116School of Information Science and Engineering, Changzhou University, Changzhou, ChinaSchool of Information Science and Engineering, Changzhou University, Changzhou, ChinaSchool of Information Science and Engineering, Changzhou University, Changzhou, ChinaThe 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.https://ieeexplore.ieee.org/document/8907879/Fuzzy systemfeature selectionrule generationBayesian modelsequential importance resampling algorithm |
| spellingShingle | Xiaoqing Gu Cong Zhang Tongguang Ni Feature Selection and Rule Generation Integrated Learning for Takagi-Sugeno-Kang Fuzzy System and its Application in Medical Data Classification IEEE Access Fuzzy system feature selection rule generation Bayesian model sequential importance resampling algorithm |
| title | Feature Selection and Rule Generation Integrated Learning for Takagi-Sugeno-Kang Fuzzy System and its Application in Medical Data Classification |
| title_full | Feature Selection and Rule Generation Integrated Learning for Takagi-Sugeno-Kang Fuzzy System and its Application in Medical Data Classification |
| title_fullStr | Feature Selection and Rule Generation Integrated Learning for Takagi-Sugeno-Kang Fuzzy System and its Application in Medical Data Classification |
| title_full_unstemmed | Feature Selection and Rule Generation Integrated Learning for Takagi-Sugeno-Kang Fuzzy System and its Application in Medical Data Classification |
| title_short | Feature Selection and Rule Generation Integrated Learning for Takagi-Sugeno-Kang Fuzzy System and its Application in Medical Data Classification |
| title_sort | feature selection and rule generation integrated learning for takagi sugeno kang fuzzy system and its application in medical data classification |
| topic | Fuzzy system feature selection rule generation Bayesian model sequential importance resampling algorithm |
| url | https://ieeexplore.ieee.org/document/8907879/ |
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