Abstract
Artificial neural networks have been an interesting alternative to use instead of classic statistical techniques, however, artificial neural networks have some disadvantages, as for example: the training process is long, the choice of topology and input variables (attributes) are difficult. This work uses three models of binomial regression (each model has a different link function) for selecting statistical significant variables for being used as input nodes on each neural network. Hybrid models were constructed, in this paper, in two steps.
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Gomes, G.S.S., Ludermir, T.B. (2006). Feature Selection for Neural Networks Through Binomial Regression. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_82
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DOI: https://doi.org/10.1007/11893257_82
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