Abstract
Fuzzy rule-based systems constitute a wide spread tool for classification 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 classifiers, 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-versus-one scheme. Besides, we will contextualize each binary classifier by adapting the database for each subproblem by means of a granularity learning process to adapt the number of fuzzy labels per variable. Our experimental study, using several datasets from KEEL dataset repository, shows the goodness of the proposed methodology.
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Acknowledgments
This work was partially supported by the Spanish Ministry of Science and Technology under project TIN-2012-33856, the Andalusian regional projects P10-TIC-06858 and P11-TIC-7765 and both the University of Jaén and Caja Rural Provincial de Jaén under project UJA2014/06/15.
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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
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