Mikel Elkano

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There are many real-world classification problems involving multiple classes, e.g., in bioinformatics, computer vision or medicine. These problems are generally more difficult than their binary counterparts. In this scenario, decomposition strategies usually improve the performance of classifiers. Hence, in this paper we aim to improve the behaviour of(More)
In this paper we present the composition of interval-valued fuzzy relations using interval-valued aggregation functions. In particular, we propose a generalization of Zadeh’s composition rule, replacing the minimum by an interval-valued aggregation function. We analyze the preservation of different properties of interval-valued fuzzy relations by this new(More)
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(More)
a Department of Computer Science, University of Jaén, 23071, Jaén, Spain b Departamento de Automática y Computación, Universidad Pública de Navarra, 31006, Pamplona, Spain c Institute of Smart Cities (ISC), Universidad Pública de Navarra, 31006, Pamplona, Spain d Faculty of Computing and Information Technology, University of Jeddah, 21589, Jeddah, Saudi(More)