DRCW-OVO: Distance-based relative competence weighting combination for One-vs-One strategy in multi-class problems

One-vs-One strategy is a common and established technique in Machine Learning to deal with multi-class classification problems. It consists of dividing the original multi-class problem into easier-to-solve binary subproblems considering each possible pair of classes. Since several classifiers are learned, their combination becomes crucial in order to predict the class of new instances. Due to the division procedure a series of difficulties emerge at this stage, such as the non-competence problem. Each classifier is learned using only the instances of its corresponding pair of classes, and hence, it is not competent to classify instances belonging to the rest of the classes; nevertheless, at classification time all the outputs of the classifiers are taken into account because the competence cannot be known a priori (the classification problem would be solved). On this account, we develop a distance-based combination strategy, which weights the competence of the outputs of the base classifiers depending on the closeness of the query instance to each one of the classes. Our aim is to reduce the effect of the non-competent classifiers, enhancing the results obtained by the state-of-the-art combinations for One-vs-One strategy. We carry out a thorough experimental study, supported by the proper statistical analysis, showing that the results obtained by the proposed method outperform, both in terms of accuracy and kappa measures, the previous combinations for One-vs-One strategy.