On the Impact of Distance-based Relative Competence Weighting Approach in One-vs-One Classification for Evolutionary Fuzzy Systems: DRCW-FH-GBML algorithm

The advantages of multi-classification schemes based on decomposition strategies, and especially the One-vs-One framework, have been stressed even for those algorithms that can address multiple classes. However, there is an inherent hitch for the One-vs-One learning scheme related to the decision process: the non-competent classifier problem. This issue refers to the case where a binary classifier outputs a score degree for a couple of classes that are not related with the input example, thus including "noise" in the score-matrix and degrading the final accuracy. For this reason, several approaches have been developed in order to address the influence of the non-competence. Among them, the distance-based combination strategy has excelled as a very robust solution. In this contribution, we aim at investigating the behaviour of this approach using Evolutionary Fuzzy Systems as baseline classifiers. We will show that the synergy between both methodologies allows a significant improvement of the results to be obtained in contrast to the standard classifier and the classical One-vs-One scheme.