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Incorporating sequential information into traditional classification models by using an element/position- sensitive SAM

A. Prinzie () and Dirk Van den Poel ()

Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium from Ghent University, Faculty of Economics and Business Administration

Abstract: The inability to capture sequential patterns is a typical drawback of predictive classification methods. This caveat might be overcome by modeling sequential independent variables by sequence-analysis methods. Combining classification methods with sequenceanalysis methods enables classification models to incorporate non-time varying as well as sequential independent variables. In this paper, we precede a classification model by an element/position-sensitive Sequence-Alignment Method (SAM) followed by the asymmetric, disjoint Taylor-Butina clustering algorithm with the aim to distinguish clusters with respect to the sequential dimension. We illustrate this procedure on a customer-attrition model as a decisionsupport system for customer retention of an International Financial-Services Provider (IFSP). The binary customer-churn classification model following the new approach significantly outperforms an attrition model which incorporates the sequential information directly into the classification method.

Keywords: sequence analysis; binary classification methods; Sequence-Alignment Method; asymmetric clustering; customer-relationship management; churn analysis (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-bec and nep-cmp
Date: 2005-02
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