The Krypteia ensemble: Designing classifier ensembles using an ancient Spartan military tradition Fumanal Idocin, J. Cordón García, Óscar Classifier ensemble Optimal classifier selection Social network Human social behaviour Multi-agent systems Sparta Krypteia In 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. 2022-12-21T12:24:11Z 2022-12-21T12:24:11Z 2022-09-30 info:eu-repo/semantics/article J. Fumanal-Idocin, O. Cordón, H. Bustince, The Krypteia ensemble: Designing classifier ensembles using an ancient Spartan military tradition, Information Fusion, Volume 90, 2023, Pages 283-297, ISSN 1566-2535, [https://doi.org/10.1016/j.inffus.2022.09.021] https://hdl.handle.net/10481/78585 10.1016/j.inffus.2022.09.021 eng http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional Elsevier