Abstract
In this work, an energy-based clustering method is used to prune heterogeneous ensembles. Specifically, the classifiers are grouped according to their predictions in a set of validation instances that are independent from the ones used to build the ensemble. In the empirical evaluation carried out, the cluster that minimizes the error in the validations set, besides reducing computational costs for storage and the prediction times, is almost as accurate as the complete ensemble. Furthermore, it outperforms subensembles that summarize the complete ensemble by including representatives from each of the identified clusters.
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Acknowledgements
The authors acknowledge financial support from the Spanish Ministry of Economy, Industry and Competitiveness, project TIN2016-76406-P.
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Cela, J., Suárez, A. (2018). Energy-Based Clustering for Pruning Heterogeneous Ensembles. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11139. Springer, Cham. https://doi.org/10.1007/978-3-030-01418-6_34
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