English, Article, Journal or magazine article edition: Incorporating sequential information into traditional classification models by using an element/position- sensitive SAM A. PRINZIE; D. VAN DEN POEL

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Bookmark: http://trove.nla.gov.au/version/15205
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Incorporating sequential information into traditional classification models by using an element/​position- sensitive SAM
Author
  • A. PRINZIE
  • D. VAN DEN POEL
Physical Description
  • preprint
Notes
  • 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.
  • sequence analysis, binary classification methods, Sequence-Alignment Method, asymmetric clustering, customer-relationship management, churn analysis
  • RePEc:rug:rugwps:05/​292
Language
  • English
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