WIT Press


Wrapped Feature Selection For Binary Classification Bayesian Regularisation Neural Networks: A Database Marketing Application

Price

Free (open access)

Paper DOI

10.2495/DATA000341

Volume

25

Pages

10

Published

2000

Size

1,079 kb

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