IVOVO: A new interval-valued one-vs-one approach for multi-class classification problems

摘要

Decomposition strategies have been shown to be a successful methodology to tackle multi-class classification problems. Among them, One-vs-One approach is a commonly used technique that consists in dividing the original multi-class problem into easier-to-solve binary sub-problems considering each possible pair of classes. However, this methodology is limited to those classifiers returning a single real value for each prediction. In this work, we present a new One-vs-One approach that is able to deal with interval-valued outputs. In order to achieve this goal, we propose applying a normalization method for intervals along with the corresponding extension of three different aggregation strategies: voting, weighted voting, and WinWV. The experimental results show the suitability of the normalization method and the improvement obtained by One-vs-One with respect to a state-of-the-art interval-valued Fuzzy Rule-Based Classification System (IVTURS).