A belief rule-based classification system using fuzzy unordered rule induction algorithm Li, Yangxue Pérez Gálvez, Ignacio Javier Cabrerizo Lorite, Francisco Javier Garg, Harish Morente Molinera, Juan Antonio This work was supported by the grant PID2022-139297OB-I00 funded by MICIU/AEI/10.13039/501100011033 and by “ERDF/EU”, by the grant 202106070037, China Scholarship Council (CSC), and by the project C-ING-165-UGR23, co-funded by the Regional Ministry of University, Research and Innovation and by the European Union under the Andalusia ERDF Programme 2021-2027. A rule-based system is a widely used artificial intelligence system that employs a set of rules to make decisions. The belief rule-based (BRB) classification system is an extension of fuzzy rule-based (FRB) system that handles uncertainty and imprecision in classification tasks by incorporating Dempster-Shafer evidence theory and fuzzy set theory. However, the BRB classification system suffers from the combinatorial explosion and high time complexity problems. To solve these issues, the fuzzy unordered rule induction algorithm (FURIA) is introduced into the BRB classification systems to design a novel BRB classification system in this paper. FURIA is computationally less complex and has superior classification performance. The proposed system outperforms BRB classification systems in terms of classification performance and significantly reduces the number of rules and conditions. Furthermore, the proposed system offers a more effective solution than FURIA. To evaluate the validity and superiority of the proposed system, we conduct two classification experiments, comparing it with five traditional classifiers and seven rule-based systems, respectively, in terms of classification performance and interpretability. 2026-01-09T09:51:31Z 2026-01-09T09:51:31Z 2024 journal article Published version: Li, Y., Pérez, I. J., Cabrerizo, F. J., Garg, H., & Morente-Molinera, J. A. (2024). A belief rule-based classification system using fuzzy unordered rule induction algorithm. Information Sciences, 667, 120462. https://doi.org/10.1016/j.ins.2024.120462 https://hdl.handle.net/10481/109369 10.1016/j.ins.2024.120462 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier