Improving Pairwise Learning Classification in Fuzzy Rule Based Classification Systems Using Dynamic Classifier Selection

Abstract

Classification based on the One-vs-One decomposition strategy has shown a high quality for addressing those problems with multiple classes, even if the learning model enables the discrimination among several concepts. The main phase of the pairwise learning is the decision process, where the outputs of the binary classifiers are combined to give a single output. Recently, it has been shown that standard decision techniques do not take into account the influence of the non-competent classifiers, i.e. those that were not trained using the class of the query example, and this can deteriorate the performance of the model. In accordance with the former, a “Dynamic Classifier Selection” for the Onevs-One approach was proposed to alleviate this issue. It basically consists of finding those classifiers whose outputs are closest to the input example, and thus remove those ones which are not related with it. In this work, we want to analyse the goodness for the former approach using a fuzzy-type baseline classifier. Experimental results show that there is in fact a significant leap in the global performance when this model is applied, both versus the standard fuzzy rule based classification system, and the One-vs-One learning approach.