Uriz Martín, Mikel XabierPaternain Dallo, DanielJurío Munárriz, AránzazuBustince Sola, HumbertoGalar Idoate, Mikel2019-12-202019-12-2020183-319-91472-31865-092910.1007/978-3-319-91476-3_36https://academica-e.unavarra.es/handle/2454/35931Trabajo presentado a la 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2018. Cádiz, 11-15 de junio de 2018In this work we study the usage of different families of fusion functions for combining classifiers in a multiple classifier system of One-vs-One (OVO) classifiers. OVO is a decomposition strategy used to deal with multi-class classification problems, where the original multi-class problem is divided into as many problems as pair of classes. In a multiple classifier system, classifiers coming from different paradigms such as support vector machines, rule induction algorithms or decision trees are combined. In the literature, several works have addressed the usage of classifier selection methods for these kinds of systems, where the best classifier for each pair of classes is selected. In this work, we look at the problem from a different perspective aiming at analyzing the behavior of different families of fusion functions to combine the classifiers. In fact, a multiple classifier system of OVO classifiers can be seen as a multi-expert decision making problem. In this context, for the fusion functions depending on weights or fuzzy measures, we propose to obtain these parameters from data. Backed-up by a thorough experimental analysis we show that the fusion function to be considered is a key factor in the system. Moreover, those based on weights or fuzzy measures can allow one to better model the aggregation problem.12 p.application/pdfeng© Springer International Publishing AG, part of Springer Nature 2018AggregationsClassification One-vs-OneFusion functionsMultiple classifier systeminfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccessAcceso abierto / Sarbide irekia