@misc{10481/88852,
year = {2016},
month = {3},
url = {https://hdl.handle.net/10481/88852},
abstract = {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.},
keywords = {One-vs- One decomposition},
title = {On the Combination of Pairwise and Granularity Learning for Improving Fuzzy Rule-Based Classification Systems: GL-FARCHD-OVO},
doi = {10.1007/978-3-319-26227-7_13},
author = {Villar Castro, Pedro and Fernández, Alberto and Herrera Triguero, Francisco},
}