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.
CITATION STYLE
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 Advances in Intelligent Systems and Computing (Vol. 403, pp. 135–146). Springer Verlag. https://doi.org/10.1007/978-3-319-26227-7_13
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