@conference {10.1007/978-3-319-19719-7_23,
	title = {Energy-Efficient Sound Environment Classifier for Hearing Aids Based on Multi-objective Simulated Annealing Programming},
	booktitle = {10th International Conference on Soft Computing Models in Industrial and Environmental Applications},
	year = {2015},
	pages = {261{\textendash}270},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	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{\textquoteright}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.},
	isbn = {978-3-319-19719-7},
	author = {Coca{\~n}a-Fern{\'a}ndez, Alberto and S{\'a}nchez, Luciano and Ranilla, Jos{\'e} and Gil-Pita, Roberto and Ayll{\'o}n, David},
	editor = {Herrero, {\'A}lvaro and Sedano, Javier and Baruque, Bruno and Quinti{\'a}n, H{\'e}ctor and Corchado, Emilio}
}
@conference {10.1007/978-3-642-02319-4_79,
	title = {A Minimum-Risk Genetic Fuzzy Classifier Based on Low Quality Data},
	booktitle = {Hybrid Artificial Intelligence Systems},
	year = {2009},
	pages = {654{\textendash}661},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {Minimum risk classification problems use a matrix of weights for defining the cost of misclassifying an object. In this paper we extend a simple genetic fuzzy system (GFS) to this case. In addition, our method is able to learn minimum risk fuzzy rules from low quality data. We include a comprehensive description of the new algorithm and discuss some issues about its fuzzy-valued fitness function. A synthetic problem, plus two real-world datasets, are used to evaluate our proposal.},
	isbn = {978-3-642-02319-4},
	author = {Palacios, Ana M. and S{\'a}nchez, Luciano and Couso, In{\'e}s},
	editor = {Corchado, Emilio and Wu, Xindong and Oja, Erkki and Herrero, {\'A}lvaro and Baruque, Bruno}
}
