A method for designing fuzzy rule-based classifier using s-function based fuzzy set guarantee interpretability
Email:
nducdu@utc.edu.vn
Keywords:
Hedge algebras, Order-based semantics, Classifier, Interpretability, Fuzzy rule-based systems
Abstract
Fuzzy rule-based systems have many practical applications. However, to extract a compact, highly accurate rule-based system and ensure its interpretability requires different methodologies and techniques, including methods of representing the semantics of linguistic words in the rule bases. Hedge algebras is utilized to create a formal basis for designing computational fuzzy-set-based semantics of linguistic words from their inherent semantics. Many studies on computational fuzzy-set-based semantic representation methods to ensure the interpretability of fuzzy rule-based systems have been proposed and applied to solve classification and regression problems. Those studies also showed that the shape of the fuzzy set affects the accuracy of the fuzzy rule-based systems. A method to design a multi-semantic structure with a fuzzy set of the form S function to ensure the interpretability of the classification systems according to Tarski's point is presented in this paper. Experimental results with 15 real standard data sets show that the proposed method obtains better classification accuracy while not increasing the complexity of the rule bases compared to the ones of the existing methodsReferences
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[2]. N. N. Quynh, N. P. Dong, N. L. Giang, H. V. Long, A new Takagi-Sugeno fuzzy system approach for fuzzy state feedback controller design and its application to malware propagation on heterogeneous complex network, Journal of Science and Technology on Information Security, 3 (2023) 43-53. https://doi.org/10.54654/isj.v3i20.988
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[4]. H. Ishibuchi, T. Yamamoto, Rule weight specification in fuzzy rule-based classification systems, IEEE Transactions on Fuzzy Systems, 13 (2005) 428–435. https://doi.org/10.1109/TFUZZ.2004.841738
[5]. C. H. Nguyen, V. T. Hoang, V. L. Nguyen, A discussion on interpretability of linguistic rule based systems and its application to solve regression problems, Knowledge-Based Systems, 88 (2015) 107–133. https://doi.org/10.1016/j.knosys.2015.08.002
[6]. V. T. Hoang, C. H. Nguyen, D. D. Nguyen, D. P. Pham, V. L. Nguyen, The interpretability and scalability of linguistic-rule-based systems for solving regression problems, International Journal of Approximate Reasoning, 149 (2022) 131-160. https://doi.org/10.1016/j.ijar.2022.07.007
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[8]. M. I. Rey, M. Galende, M. J. Fuente, G. I. Sainz-Palmero, Multi-objective based Fuzzy Rule Based Systems (FRBSs) for trade-off improvement in accuracy and interpretability: A rule relevance point of view, Knowledge-Based Systems, 127 (2017) 67–84. https://doi.org/10.1016/j.knosys.2016.12.028
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[10]. C. Mencar, A.M. Fanelli, Interpretability constraints for fuzzy information granulation, Information Sciences, 178 (2008) 4585–4618. https://doi.org/10.1016/j.ins.2008.08.015
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[12]. C. H. Nguyen, W. Wechler, Extended hedge algebras and their application to fuzzy logic, Fuzzy Sets and Systems, 52 (1992) 259–281. https://doi.org/10.1016/0165-0114(92)90237-X
[13]. C. H. Nguyen, V. L. Nguyen, Fuzziness measure on complete hedges algebras and quantifying semantics of terms in linear hedge algebras, Fuzzy Sets and Systems, 158 (2007) 452-471. https://doi.org/10.1016/j.fss.2006.10.023
[14]. A. Tarski, A. Mostowski, and R. Robinson, Undecidable Theories. North-Holland, 1953.
[15]. D. P. Pham, V. T. Hoang, D. D. Nguyen, Biểu diễn ngữ nghĩa tính toán đảm bảo tính giải nghĩa của hệ phân lớp dựa trên luật mờ, Tạp chí Khoa học và Công nghệ - Đại học Thái Nguyên, 227 (2022) 107 - 114. https://doi.org/10.1016/j.knosys.2014.04.047
[16]. C. H. Nguyen, T. S. Tran, D. P. Pham, Modeling of a semantics core of linguistic terms based on an extension of hedge algebra semantics and its application, Knowledge-Based Systems, 67 (2014) 244–262. https://doi.org/10.1016/j.knosys.2014.04.047
[17]. C. H. Nguyen, W. Pedrycz, T. L. Duong, T. S. Tran, A genetic design of linguistic terms for fuzzy rule based classifiers, International Journal of Approximate Reasoning, 54 (2013) 1-21. https://doi.org/10.1016/j.ijar.2012.07.007
[18]. V. T. Hoang, D. D. Nguyen, C. H. Nguyen, Một phương pháp thiết kế ngữ nghĩa dạng tập mờ của từ ngôn ngữ dựa trên đại số gia tử mở rộng và ứng dụng xây dựng FRBS giải bài toán hồi qui, Chuyên san Các công trình nghiên cứu, phát triển và ứng dụng Công nghệ Thông tin và Truyền thông, 38 (2017) 51-57. https://doi.org/10.32913/rd-ict.vol2.no38.527
[19]. D. D. Nguyen, D. P. Pham, D. V. Pham, D. T. Nguyen, Một phương pháp thiết kế ngữ nghĩa tính toán của các từ ngôn ngữ giải bài toán phân lớp dựa trên luật mờ, Chuyên san Các công trình nghiên cứu, phát triển và ứng dụng Công nghệ Thông tin và Truyền thông, 1 (2020) 9-18. https://doi.org/10.32913/mic-ict-research-vn.v2020.n1.914
[20]. J. Demˇsar, Statistical Comparisons of Classifiers over Multiple Data Sets, Journal of Machine Learning Research, 7 (2006) 1–30.
[21]. D. P. Pham, C. H. Nguyen, T. T. Nguyen, Multi-objective Particle Swarm Optimization Algorithm and its Application to the Fuzzy Rule Based Classifier Design Problem with the Order Based Semantics of Linguistic Terms, In Proceedings of the 10th IEEE RIVF International Conference on Computing and Communication Technologies (RIVF-2013), Hanoi, Vietnam 2013, 12–17. https://doi.org/10.1109/RIVF.2013.6719858
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Received
10/03/2024
Revised
03/04/2024
Accepted
05/04/2024
Published
15/04/2024
Type
Research Article
How to Cite
Nguyễn Đức , D. (1713114000). A method for designing fuzzy rule-based classifier using s-function based fuzzy set guarantee interpretability. Transport and Communications Science Journal, 75(3), 1335-1347. https://doi.org/10.47869/tcsj.75.3.2
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