On the Combination of Pairwise and Granularity Learning for Improving Fuzzy Rule-Based Classification Systems: GL-FARCHD-OVO Villar Castro, Pedro Fernández, Alberto Herrera Triguero, Francisco One-vs- One decomposition Fuzzy rule-based systems constitute a very spread tool for classi cation problems, but several proposals may decrease its performance when dealing with multi-class problems. Among existing approaches, the FARC-HD algorithm has excelled as it has shown to achieve accurate and compact classi ers, even in the context of multi-class problems. In this work, we aim to go one step further to improve the behavior of the former algorithm by means of a "divide-and-conquer" approach, via binarization in a one-vs-one scheme. Besides, we will contextualize each binary classi er by adapting the data base for each subproblem by means of a granularity learning process to adapt the number of fuzzy labels per variable. Our experimental study, using several data-sets from KEEL data-set repository, shows the goodness of the proposed methodology. 2024-02-09T11:13:13Z 2024-02-09T11:13:13Z 2016-03-05 info:eu-repo/semantics/bookPart Villar, P., Fernández, A., Herrera, F. (2016). On the Combination of Pairwise and Granularity Learning for Improving Fuzzy Rule-Based Classification Systems: GL-FARCHD-OVO. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-319-26227-7_13 https://hdl.handle.net/10481/88852 10.1007/978-3-319-26227-7_13 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivatives 4.0 Internacional