Fumanal Idocin, Javier
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Fumanal Idocin
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Javier
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Estadística, Informática y Matemáticas
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Publication Open Access The Krypteia ensemble: designing classifier ensembles using an ancient Spartan military tradition(Elsevier, 2023) Fumanal Idocin, Javier; Cordón, Óscar; Bustince Sola, Humberto; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate PublikoaIn this work we propose a new algorithm to train and optimize an ensemble of classifiers. We call this algorithm the Krypteia ensemble, based on an ancient Spartan tradition designed to convert their most promising individuals into future leaders of their society. We show how to adapt this ancient custom to optimize classifiers by generating different variations of the same task, each one offering different hardships according to distinct stochastic variables. This is thus applied to induce diversity in the set of individual weak learners. Then, we use a set of agents designed to select those subjects who excel in their assignments, and whose interaction minimizes excessive redundancies in the resulting population. We also study how different Krypteia ensembles can be stacked together, so that more complex classifiers can be built using the same procedure. Besides, we consider a wide range of different aggregation functions in the decision making phase to find the optimal performance for the different Krypteia ensemble variations tested. Finally, we study how different Krypteia ensembles perform for a wide range of classification datasets and we compare them with other state-of-the-art design techniques of classifier ensembles, obtaining favourable results to our proposal.Publication Open Access Community detection and social network analysis based on the Italian wars of the 15th century(Elsevier, 2020) Fumanal Idocin, Javier; Alonso Betanzos, Amparo; Cordón, Óscar; Bustince Sola, Humberto; Minárová, María; Institute of Smart Cities - ISCIn this contribution we study social network modelling by using human interaction as a basis. To do so, we propose a new set of functions, affinities, designed to capture the nature of the local interactions among each pair of actors in a network. By using these functions, we develop a new community detection algorithm, the Borgia Clustering, where communities naturally arise from the multi-agent interaction in the network. We also discuss the effects of size and scale for communities regarding this case, as well as how we cope with the additional complexity present when big communities arise. Finally, we compare our community detection solution with other representative algorithms, finding favourable results.