Application of Gradient Boosting in the Design of Fuzzy Rule-Based Regression Models

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorZhang, Huimin
dc.contributor.authorHu, Xingchen
dc.contributor.authorZhu, Xiubin
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2025-06-04T08:20:56Z
dc.date.available2025-06-04T08:20:56Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractThis study is devoted to the design of gradient boosted fuzzy rule-based models for regression problems. Fuzzy rule-based models are built on the basis of information granules formed in the input and output spaces whose structure involves a family of conditional 'if-then' statements. The architecture of fuzzy rule-based models contributes to the realization of a sound tradeoff between modeling accuracy and interpretability and computing overhead. Gradient boosting paradigm has emerged as a powerful learning method realized through sequentially fitting additive base learners to current residuals in the steepest descent way. However, surprisingly, studies on the design and analysis of gradient boosted fuzzy rule-based models are still lacking. In this study, fuzzy rule-based model is regarded as a base learner. Different loss functions and their influence on the performance of the final models are explored. We also thoroughly investigate an impact of the initial quality of the rule-based model (implied by the number of rules) on the process of gradient boosting. The performance of the proposed approach is illustrated by a series of experimental studies concerning synthetic and publicly available datasets. © 2024 IEEE.
dc.description.sponsorshipManuscript received 21 January 2024; revised 9 April 2024; accepted 16 April 2024. Date of publication 6 May 2024; date of current version 27 September 2024. This work was supported in part by the National Natural Science Foundation of China under Grant 62076189, Grant 62103114 and Grant U21A20474, and in part by Guangxi Science and Technology Project under Grant GuikeAA22067070 and Grant GuikeAD21220114, and Guangxi \u201CBagui Scholar\u201D Teams for Innovation and Research Project, Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing, and Guangxi Talent Highland Project of Big Data Intelligence and Application. Recommended for acceptance by Liqiang Nie. (Corresponding author: Xingchen Hu.) Huimin Zhang is with the Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin 541004, China, and also with the Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin 541004, China (e-mail: hmzh@mailbox.gxnu.edu.cn).
dc.identifier.citationZhang, H., Hu, X., Zhu, X., Liu, X., & Pedrycz, W. (2024). Application of Gradient Boosting in the Design of Fuzzy Rule-Based Regression Models. IEEE Transactions on Knowledge and Data Engineering.
dc.identifier.doi10.1109/TKDE.2024.3392247
dc.identifier.endpage5632
dc.identifier.issn10414347
dc.identifier.issue11
dc.identifier.scopusqualityQ1
dc.identifier.startpage5621
dc.identifier.urihttp://dx.doi.org/10.1109/TKDE.2024.3392247
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7291
dc.identifier.volume36
dc.identifier.wosWOS:001336378400058
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherIEEE Computer Society
dc.relation.ispartofIEEE Transactions on Knowledge and Data Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.titleApplication of Gradient Boosting in the Design of Fuzzy Rule-Based Regression Models
dc.typeArticle

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