Wrapped Feature Selection For Binary
Classification Bayesian Regularisation Neural
Networks: A Database Marketing Application
Author(s)
S. Viaene, B. Baesens, D. Van den Poel, G. Dedene,
J. Vandenbulcke & J. Vanthienen
Abstract
In this paper, we try to validate existing theory on and develop additional insight
into repeat purchasing behaviour in a direct-marketing setting by means of an
illuminating case study. The case involves the detection and qualification of the
most relevant RFM (Recency, Frequency and Monetary) features, using a
wrapped feature selection method in a neural network context. Results indicate
that elimination of redundant/irrelevant features by means of the discussed
feature selection method, allows to significantly reduce model complexity
without degrading generalisation ability. It is precisely this issue that will allow
to infer some very interest
Keywords
Related Book
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