Abstract
It is known that when dealing with interval-valued data, there exist problems associated with the non-existence of a total order. In this work we investigate a reformulation of an interval-valued decomposition strategy for multi-class problems called IVOVO, and we analyze the effectiveness of considering different admissible orders in the aggregation phase of IVOVO. We demonstrate that the choice of an appropriate admissible order allows the method to obtain significant differences in terms of accuracy.
This work has been partially supported by the Spanish Ministry of Science and Technology under the project TIN2016-77356-P and the Public University of Navarre under the project PJUPNA13.
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Uriz, M., Paternain, D., Bustince, H., Galar, M. (2019). On the Influence of Admissible Orders in IVOVO. In: Pérez GarcÃa, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado RodrÃguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_31
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