A Design of Fuzzy Rule-Based Classifier for Multiclass Classification and Its Realization in Horizontal Federated Learning

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorHu, Xingchen
dc.contributor.authorZhu, Xiubin
dc.contributor.authorYang, Lan
dc.contributor.authorPedrycz, Witold
dc.contributor.authorLi, Zhiwu
dc.date.accessioned2025-04-16T20:39:02Z
dc.date.available2025-04-16T20:39:02Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractPattern recognition plays an important role in the process of knowledge discovery. The construction of easily describable and interpretable classification rules is of vital importance in pattern recognition. In this study, we propose a development of fuzzy rule-based classifier for multiclass classification problems and elaborate on a privacy-preserving realization of the proposed methodology in the presence of decentralized datasets. Fuzzy rule-based models provide an effective and efficient alternative for characterizing the complex relationship between the input variables and target classes. An overall design process of the proposed classifier consists of two main phases: (a) formation of information granules (clusters) to reveal the underlying structure of the training data, and (b) construction of local classification rules whose outputs reflect the probability distribution of the input data over all the classes. The constructed information granules form a backbone of the architecture of the classifier while the optimization of the parameters of local rules is carried out through using a gradient descent method with the guidance of the cross-entropy loss function. Furthermore, a federated gradient-based optimization mechanism is utilized to construct fuzzy classifier in a privacy-preserving approach. The originalities of the proposed methodology are twofold: first, a design of fuzzy classifier through the synergy of cluster-centric architecture and the cross-entropy loss function is presented. Second, we augment the proposed fuzzy classifier based on the concept of federated learning such that it can learn from distributed data without sacrificing data security and confidentiality. Experiments are carried out on a two-dimensional synthetic dataset and a number of real-world datasets. Experimental results show the excellent classification capability of the proposed classifier realized in the centralized way and in the federated learning environment. © 1993-2012 IEEE.
dc.description.sponsorshipFunding text 1 This work was supported in part by the National Natural Science Foundation of China under Grant 62076189, Grant 62302364, and Grant 62376279, in part by the Ministry of Education and Science in Poland, in part by the targeted subsidy for theSYBIOZProject CL/631/2021/DF/DW, in part by the President of the Lukasiewicz Research Network Center, in part by the Recruitment Program of Global Experts, in part by the Science and Technology Development Fund, and in part by MSAR under Grant 0012/2019/A1. Funding text 2 Manuscript received 17 April 2024; revised 29 May 2024; accepted 7 June 2024. Date of publication 11 June 2024; date of current version 4 September 2024. This work was supported in part by the National Natural Science Foundation of China under Grant 62076189, Grant 62302364, and Grant 62376279, in part by the Ministry of Education and Science in Poland, in part by the targeted subsidy for the SYBIOZ Project C\u0141/631/2021/DF/DW, in part by the President of the \u0141ukasiewicz Research Network Center, in part by the Recruitment Program of Global Experts, in part by the Science and Technology Development Fund, and in part by MSAR under Grant 0012/2019/A1. Recommended by Associate Editor A. M. Wilbik. (Corresponding author: Zhiwu Li.) Xingchen Hu is with the College of Systems Engineering, National University of Defense Technology, Changsha 410073, China (e-mail: xhu4@ualberta.ca).
dc.identifier.citationHu, X., Zhu, X., Yang, L., Pedrycz, W., & Li, Z. (2024). A Design of Fuzzy Rule-based Classifier for Multi-class Classification and its Realization in Horizontal Federated Learning. IEEE Transactions on Fuzzy Systems.
dc.identifier.doi10.1109/TFUZZ.2024.3412983
dc.identifier.endpage5108
dc.identifier.issn10636706
dc.identifier.issue9
dc.identifier.scopusqualityQ1
dc.identifier.startpage5098
dc.identifier.urihttp://dx.doi.org/10.1109/TFUZZ.2024.3412983
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6081
dc.identifier.volume32
dc.identifier.wosWOS:001307418200009
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE Transactions on Fuzzy Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectClassification
dc.subjectCross Entropy
dc.subjectFuzzy Rule-Based Classifier
dc.subjectSoftmax
dc.subjectHorizontal Federated Learning
dc.titleA Design of Fuzzy Rule-Based Classifier for Multiclass Classification and Its Realization in Horizontal Federated Learning
dc.typeArticle

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