Enhancing evolutionary fuzzy systems for multi-class problems: Distance-based relative competence weighting with truncated confidences (DRCW-TC)

Abstract

Classification problems with multiple classes suppose a challenge in Data Mining tasks. There is a difficulty inherent to the learning process when trying to find the most adequate discrimination functions among the different concepts within the dataset. Using Fuzzy Rule Based Classification Systems in general, and Evolutionary Fuzzy Systems in particular, provide the advantage of describing smoother borderline areas, thanks to the linguistic label-based representation.In multi-classification, the pairwise learning approach (One-vs-One) has gained a notorious attention. However, there is certain dependence between the goodness of the confidence degrees or scores of binary classifiers, and the final performance shown by the global model. Regarding this fact, the problem of non-competent classifiers is of special relevance. It occurs when a binary classifier outputs a positive score for a couple of classes unrelated with the input example, which may degrade the final accuracy. Precisely, the previously exposed properties of fuzzy classifiers make them more prone to the former condition.In this paper, we propose an extension of the distance-based combination strategy to overcome this non-competence problem. It is based on the truncation of the confidence degrees of the classes prior to the distance-based tuning. This allows taking advantage of the good classification abilities of Evolutionary Fuzzy Systems, while diminishing the adverse effect of the aforementioned non-competence. Experimental results, using FARC-HD with overlap functions as the fuzzy learning algorithm, show that this new adaptation of the Distance-based Relative Competence Weighting model outperforms both the OVO and standard distance-based approaches, and it is competitive with robust classifiers such as Support Vector Machines.