Abstract
Data mining applications addressing classification problems must master two key tasks: feature selection and model selection. This paper proposes a random feature selection procedure integrated within the multinomial logit (MNL) classifier to perform both tasks simultaneously. We assess the potential of the random feature selection procedure (exploiting randomness) as compared to an expert feature selection method (exploiting domain-knowledge) on a CRM cross-sell application. The results show great promise as the predictive accuracy of the integrated random feature selection in the MNL algorithm is substantially higher than that of the expert feature selection method.
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Prinzie, A., Van den Poel, D. (2006). Exploiting Randomness for Feature Selection in Multinomial Logit: A CRM Cross-Sell Application. In: Perner, P. (eds) Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining. ICDM 2006. Lecture Notes in Computer Science(), vol 4065. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11790853_25
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DOI: https://doi.org/10.1007/11790853_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36036-0
Online ISBN: 978-3-540-36037-7
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