Abstract
Accuracy alone is insufficient to evaluate the performance of a classifier especially when the number of classes increases. This paper proposes an approach to deal with multi-class problems based on Accuracy (C) and Sensitivity (S). We use the differential evolution algorithm and the ELM-algorithm (Extreme Learning Machine) to obtain multi-classifiers with a high classification rate level in the global dataset with an acceptable level of accuracy for each class. This methodology is applied to solve four benchmark classification problems and obtains promising results.
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Sánchez-Monedero, J., Hervás-Martínez, C., Martínez-Estudillo, F.J., Ruz, M.C., Moreno, M.C.R., Cruz-Ramírez, M. (2010). Evolutionary Learning Using a Sensitivity-Accuracy Approach for Classification. In: Corchado, E., Graña Romay, M., Manhaes Savio, A. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13803-4_36
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DOI: https://doi.org/10.1007/978-3-642-13803-4_36
Publisher Name: Springer, Berlin, Heidelberg
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