Uriz Martín, Mikel XabierPaternain Dallo, DanielBustince Sola, HumbertoGalar Idoate, Mikel2021-03-042021-03-042019978-3-030-21920-8 (Electronic)10.1007/978-3-030-21920-8_5https://academica-e.unavarra.es/handle/2454/39329Trabajo presentado al Joint World Congress of the International-Fuzzy-Systems-Assoc (IFSA) and the Annual Conference of the North-American-Fuzzy-Information-Proc-Soc (NAFIPS) / 12th International Workshop on Constraint Programming and Decision Making (CoProd) (JUN 17-21, 2019) Lafayette, USA.IVOVO stands for Inverval-Valued One-Vs-One and is the combination of IVTURS fuzzy classifier and the One-Vs-One strategy. This method is designed to improve the performance of IVTURS in multi-class problems, by dividing the original problem into simpler binary ones. The key issue with IVTURS is that interval-valued confidence degrees for each class are returned and, consequently, they have to be normalized for applying a One-Vs-One strategy. However, there is no consensus on which normalization method should be used with intervals. In IVOVO, the normalization method based on the upper bounds was considered as it maintains the admissible order between intervals and also the proportion of ignorance, but no further study was developed. In this work, we aim to extend this analysis considering several normalizations in the literature. We will study both their main theoretical properties and empirical performance in the final results of IVOVO.13 p.application/pdfeng© Springer Nature Switzerland AG 2019IVOVOMulti-class problemsInterval normalizationinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccess