Abstract
A methodology for designing classifiers through multicriteria metaheuristics is introduced. The purpose of the new method is to jointly optimize the expected total classification cost and the energy consumption of the device the classifier is implemented on. A numerical study is provided, where different alternatives are implemented on a hearing aid. This aid is capable of automatically classifying the acoustic environment that surrounds the user and choosing the parameters of the amplification that are best adapted to the user’s comfort. The proposed method attains relevant improvements in energy consumption with small to negligible loss in classification accuracy with respect to a selection of algorithms.
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Notes
- 1.
MOEA Framework, a Java library for multiobjective evolutionary algorithms, http://www.moeaframework.org/.
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Acknowledgments
This work has been partially supported by “Ministerio de Economía y Competitividad” from Spain/FEDER under grants TIN2011-24302, TIN2014-56967-R, TEC2012-38142-C04-02 and TEC2012-38142-C04-04, and the Regional Ministry of the Principality of Asturias under grant FC-15-GRUPIN14-073.
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Cocaña-Fernández, A., Sánchez, L., Ranilla, J., Gil-Pita, R., Ayllón, D. (2015). Energy-Efficient Sound Environment Classifier for Hearing Aids Based on Multi-objective Simulated Annealing Programming. In: Herrero, Á., Sedano, J., Baruque, B., Quintián, H., Corchado, E. (eds) 10th International Conference on Soft Computing Models in Industrial and Environmental Applications. Advances in Intelligent Systems and Computing, vol 368. Springer, Cham. https://doi.org/10.1007/978-3-319-19719-7_23
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DOI: https://doi.org/10.1007/978-3-319-19719-7_23
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