Enhancing Fuzzy Rule Based Systems in Multi-Classification Using Pairwise Coupling with Preference Relations

This contribution proposes a technique for Fuzzy Rule Based Classification Systems (FRBCSs) based on a multi-classifier approach using fuzzy preference relations for dealing with multi-class classification. The idea is to decompose the original data-set into binary classification problems using a pairwise coupling approach (confronting all pair of classes), and to obtain a fuzzy system for each one of them. Along the inference process, each FRBCS generates an association degree for its two classes, and these values are encoded into a fuzzy preference relation. The final class of the whole FRBCS will be obtained by decision making following a non-dominance criterium. We show the goodness of our proposal in contrast with the base fuzzy model with an extensive experimental study following a statistical study for analysing the differences in performance among the algorithms. We will also contrast our results versus the well-known C4.5 decision tree.