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.
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References
Bertoluzza, C., Gil, M.A., Ralescu, D.A. (eds.): Statistical Modeling, Analysis and Management of Fuzzy Data. Springer, Heidelberg (2003)
Bortolan, G., Degani, R.: A review of some methods for ranking fuzzy subsets. Fuzzy Sets and Systems 15, 1–19 (1985)
Cordón, O., Herrera, F., Hoffmann, F., Magdalena, L.: Genetic fuzzy systems. In: Evolutionary tuning and learning of fuzzy knowledge bases. World Scientific, Singapore (2001)
Fernandez, A., Garcia, S., del Jesus, M.J., Herrera, F.: A Study of the Behaviour of Linguistic Fuzzy Rule Based Classification Systems in the Framework of Imbalanced Data Sets. Fuzzy Sets and Systems 159(18), 2378–2398 (2008)
Hand, D.J.: Discrimination and Classification. Wiley, Chichester (1981)
Herrera, F.: Genetic Fuzzy Systems: Taxonomy, Current Research Trends and Prospects. Evolutionary Intelligence 1, 27–46 (2008)
Ishibuchi, H., Nakashima, T., Murata, T.: A fuzzy classifier system that generates fuzzy if-then rules for pattern classification problems. In: Proc. of 2nd IEEE CEC, pp. 759–764 (1995)
Palacios, A., Sánchez, L., Couso, I.: A baseline genetic fuzzy classifier based on low quality data. IFSA-EUSFLAT (2009) (Submitted)
Pulkkinen, P., Hytönen, J., Koivisto, H.: Developing a bioaerosol detector using hybrid genetic fuzzy systems. Engineering Applications of Artificial Intelligence 21(8), 1330–1346 (2008)
Sánchez, L., Couso, I.: Advocating the use of imprecisely observed data in genetic fuzzy systems. IEEE Transactions on Fuzzy Systems 15(4), 551–562 (2007)
Sánchez, L., Otero, J., Couso, I.: Obtaining linguistic fuzzy rule-based regression models from imprecise data with multiobjective genetic algorithms. Soft Computing 13(5), 467–479 (2008)
Van Broekhoven, E., Adriaenssens, V., De Baets, B.: Interpretability-preserving genetic optimization of linguistic terms in fuzzy models for fuzzy ordered classification: An ecological case study. International Journal of Approximate Reasoning 44(1), 65–90 (2007)
Verschae, R., Del Solar, J.R., Köppen, M., Garcia, R.V.: Improvement of a face detection system by evolutionary multi-objective optimization. In: Proc. HIS 2005, pp. 361–366 (2005)
Wu, B., Sun, C.: Interval-valued statistics, fuzzy logic, and their use in computational semantics. Journal of Intelligent and Fuzzy Systems 1–2(11), 1–7 (2001)
Teredesai, A., Govindaraju, V.: GP-based secondary classifiers. Pattern Recognition. 38(4), 505–512 (2005)
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© 2009 Springer-Verlag Berlin Heidelberg
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Palacios, A.M., Sánchez, L., Couso, I. (2009). A Minimum-Risk Genetic Fuzzy Classifier Based on Low Quality Data. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds) Hybrid Artificial Intelligence Systems. HAIS 2009. Lecture Notes in Computer Science(), vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_79
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DOI: https://doi.org/10.1007/978-3-642-02319-4_79
Publisher Name: Springer, Berlin, Heidelberg
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