one-step dynamic classifier ensemble model for customer value segmentation with missing values

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ID: 149864
2014
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Abstract
Scientific customer value segmentation (CVS) is the base of efficient customer relationship management, and customer credit scoring, fraud detection, and churn prediction all belong to CVS. In real CVS, the customer data usually include lots of missing values, which may affect the performance of CVS model greatly. This study proposes a one-step dynamic classifier ensemble model for missing values (ODCEM) model. On the one hand, ODCEM integrates the preprocess of missing values and the classification modeling into one step; on the other hand, it utilizes multiple classifiers ensemble technology in constructing the classification models. The empirical results in credit scoring dataset “German” from UCI and the real customer churn prediction dataset “China churn” show that the ODCEM outperforms four commonly used “two-step” models and the ensemble based model LMF and can provide better decision support for market managers.
Reference Key
xiao2014mathematicalone-step Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Jin Xiao;Bing Zhu;Geer Teng;Changzheng He;Dunhu Liu
Journal journal of power sources
Year 2014
DOI
10.1155/2014/869628
URL
http://dx.doi.org/10.1155/2014/869628
Keywords
civil engineering (general)mathematics engineering (general)

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