The Krypteia ensemble: Designing classifier ensembles using an ancient Spartan military tradition
Metadata
Show full item recordEditorial
Elsevier
Materia
Classifier ensemble Optimal classifier selection Social network Human social behaviour Multi-agent systems Sparta Krypteia
Date
2022-09-30Referencia bibliográfica
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]
Sponsorship
AEI PID2019-108392; Spanish Government PID2021-122916NB-I00; Regional Government of Andalusia under grant EXAISFI P18-FR-4262; European Commission Universidad Publica de NavarraAbstract
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.