@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}
}
