Abstract
The classification of survival in severe burnt patients is an on-going problem. In this paper we propose a multiobjective optimisation model with constraints to obtain fuzzy classification models based on the criteria of accuracy and interpretability. We also describe a multiobjective evolutionary approach for fuzzy classification based on data with real and discrete attributes. This approach is evaluated using three different evolutive schemas: pre-selection with niches, NSGA-II and ENORA. The results are compared as regards efficacy by statistical techniques.
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Jiménez, F., Sánchez, G., Juárez, J.M., Alcaraz, J.M., Sánchez, J.F. (2009). Fuzzy Classification of Mortality by Infection of Severe Burnt Patients Using Multiobjective Evolutionary Algorithms. In: Mira, J., Ferrández, J.M., Álvarez, J.R., de la Paz, F., Toledo, F.J. (eds) Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira’s Scientific Legacy. IWINAC 2009. Lecture Notes in Computer Science, vol 5601. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02264-7_46
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DOI: https://doi.org/10.1007/978-3-642-02264-7_46
Publisher Name: Springer, Berlin, Heidelberg
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