2026-10-23T16:02:05Zhttps://gumushane-rdlopen.rawdatalibrary.net/oai/request

oai:gumushane-rdlopen.rawdatalibrary.net:gumushane/117482026-10-22T11:34:59Zopenairecom_gumushane_10000col_gumushane_10001com_gumushane_10007col_gumushane_10171

A Novel BPA Derivation Method Using Linguistic Fuzzy Sets in Dempster-Shafer TheoryBaşkan, ElifŞahin, RıdvanKahraman, C.Oztaysi, B.Cebi, S.Cevik Onar, S.Tolga, C.Ucal Sari, I.Otay, I.Basic Probability AssignmentDempster-ShaferLinguistic Fuzzy SetsIn this study, we present a new method for developing Basic Probability Assignment (BPA) using Linguistic Fuzzy Sets (LFS). Dempster-Shafer Theory (DST) is used to efficiently combine different data sources. The use of LFS provides a robust structure for managing uncertainty in decision making by representing knowledge in linguistic terms. However, effectively integrating multiple uncertain sources requires a strong probabilistic approach. To overcome this challenge, we propose a method to derive BPA values directly from linguistic terms, ensuring compatibility with the DST fusion rule. We then use the Dempster-Shafer combination rule to combine information from various sources, thereby improving the reliability of the decision-making process.Springer Science and Business Media Deutschland GmbH2025Conference Objecthttps://rdlopen.rawdatalibrary.net/r/gumushane/11748https://doi.org/10.1007/978-3-031-97985-9_10978-303197984-22367-3370enLecture Notes in Networks and Systemsinfo:eu-repo/semantics/closedAccessinfo:eu-repo/semantics/conferenceObjecthttps://gumushane-rdlopen.rawdatalibrary.net/tr/items/gumushane/11748