A Minimum-Risk Genetic Fuzzy Classifier Based on Low Quality Data

Authors:
Ana M. Palacios;Luciano Sánchez;Inés Couso
Affiliations:
Dpto. Informática, Univ. Oviedo, Gijón, Asturias, Spain;Dpto. Informática, Univ. Oviedo, Gijón, Asturias, Spain;Dpto. de Estadística e I.O. y D.M, Univ. Oviedo, Gijón, Asturias, Spain
Venue:
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Year:
2009

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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.