An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorLi, Zhuolin
dc.contributor.authorZhang, Zhen
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2025-04-17T08:21:22Z
dc.date.available2025-04-17T08:21:22Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractLeveraging assignment example preference information, to determine the shape of marginal utility functions and category thresholds of the threshold-based multi-criteria sorting (MCS) model, has emerged as a focal point of current research within the realm of MCS. Most studies assume decision makers can provide all assignment example preference information in batch and that their preferences over criteria are monotonic, which may not align with practical MCS problems. This paper introduces a novel incremental preference elicitation- based approach to learning potentially non-monotonic preferences in MCS problems, enabling decision makers to progressively provide assignment example preference information. Specifically, we first construct a max- margin optimization-based model to model potentially non-monotonic preferences and inconsistent assignment example preference information in each iteration of the incremental preference elicitation process. Using the optimal objective function value of the max-margin optimization-based model, we devise information amount measurement methods and question selection strategies to pinpoint the most informative alternative in each iteration within the framework of uncertainty sampling inactive learning. Once the termination criterion is satisfied, the sorting result for non-reference alternatives can be determined through the use of two optimization models, i.e., the max-margin optimization-based model and the complexity controlling optimization model. Subsequently, two incremental preference elicitation-based algorithms are developed to learn potentially non-monotonic preferences, considering different termination criteria. Ultimately, we apply the proposed approach to a firm financial state rating problem to elucidate the detailed implementation steps, and perform computational experiments on both artificial and real-world data sets to compare the proposed question selection strategies with several benchmark strategies.
dc.description.sponsorshipNational Natural Science Foundation of China Fundamental Research Funds for the Central Universities Scientific Research Fund of Liaoning Provincial Education Department Ministry of Education of the People's Republic of China
dc.identifier.citationLi, Z., Zhang, Z., & Pedrycz, W. (2025). An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting. European Journal of Operational Research, 323(2), 553-570.
dc.identifier.doi10.1016/j.ejor.2024.11.047
dc.identifier.endpage570
dc.identifier.issn0377-2217
dc.identifier.issn1872-6860
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85212239512
dc.identifier.scopusqualityQ1
dc.identifier.startpage553
dc.identifier.urihttp://dx.doi.org/10.1016/j.ejor.2024.11.047
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6161
dc.identifier.volume323
dc.identifier.wosWOS:001439641500001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherElsevier b.v.
dc.relation.ispartofEuropean journal of operational research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectActive Learning
dc.subjectMulti-Criteria Sorting
dc.subjectNon-Monotonic Preferences
dc.subjectPreference Elicitation
dc.subjectPreference Learning
dc.titleAn incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting
dc.typeArticle

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