@misc{10481/78585,
year = {2022},
month = {9},
url = {https://hdl.handle.net/10481/78585},
abstract = {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.},
organization = {AEI	PID2019-108392},
organization = {Spanish Government		
PID2021-122916NB-I00},
organization = {Regional Government of Andalusia under grant EXAISFI	P18-FR-4262},
organization = {European Commission		
Universidad Publica de Navarra},
publisher = {Elsevier},
keywords = {Classifier ensemble},
keywords = {Optimal classifier selection},
keywords = {Social network},
keywords = {Human social behaviour},
keywords = {Multi-agent systems},
keywords = {Sparta},
keywords = {Krypteia},
title = {The Krypteia ensemble: Designing classifier ensembles using an ancient Spartan military tradition},
doi = {10.1016/j.inffus.2022.09.021},
author = {Fumanal Idocin, J. and Cordón García, Óscar},
}