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• W2003034199 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 sequence-analysis 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 decision-support 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." @default.
• W2003034199 created "2016-06-24" @default.
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• W2003034199 date "2006-11-01" @default.
• W2003034199 modified "2023-10-17" @default.
• W2003034199 title "Incorporating sequential information into traditional classification models by using an element/position-sensitive SAM" @default.
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• W2003034199 doi "https://doi.org/10.1016/j.dss.2005.02.004" @default.
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