Abstract
In this chapter, we present an extension of the wrapper approach applied to the predictor. The originality is to use the area under the training lift curve as a criterion of feature set optimality and to preprocess the numeric variables with a new optimal discretization method. The method is experimented on the NIPS 2003 datasets both as a wrapper and as a filter for multi-layer perceptron.
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Boullé, M. (2006). An Enhanced Selective Naïve Bayes Method with Optimal Discretization. In: Guyon, I., Nikravesh, M., Gunn, S., Zadeh, L.A. (eds) Feature Extraction. Studies in Fuzziness and Soft Computing, vol 207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-35488-8_26
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DOI: https://doi.org/10.1007/978-3-540-35488-8_26
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