Incorporating sequential information into traditional classification models by using an element/position- sensitive SAM
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.
|Date of creation:||Feb 2005|
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- Alineamiento de secuencias in Wikipedia Spanish ne '')
- Dizi hizalaması in Wikipedia Turkish ne '')
- Aliñamento de secuencias in Wikipedia Galician ne '')
- 聚类分析 in Wikipedia Chinese ne '')
- Sequence alignment in Wikipedia English ne '')
- Customer attrition in Wikipedia English ne '')
- User:Webridge/我的沙盘/2 in Wikipedia Chinese ne '')
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