Abstract
In this paper, the behavior of Modular and Non-Modular Neural Networks trained with the classical backpropagation algorithm in batch mode and applied to classification problems with Multi-Class imbalance is studied. Three different cost functions are introduced in the training algorithm in order to solve the problem in four different databases. The proposed strategies show an improvement in the classification accuracy with three different types of Neural Networks.
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Alejo, R., Sotoca, J.M., Valdovinos, R.M., Casañ, G.A. (2009). The Multi-Class Imbalance Problem: Cost Functions with Modular and Non-Modular Neural Networks. In: Wang, H., Shen, Y., Huang, T., Zeng, Z. (eds) The Sixth International Symposium on Neural Networks (ISNN 2009). Advances in Intelligent and Soft Computing, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01216-7_44
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DOI: https://doi.org/10.1007/978-3-642-01216-7_44
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