Abstract
Excessive lose of customer account is becoming a major headache for VIP E-Mail hosting companies. Analysis of what kind of customer is more prone to lose and finding the appropriate measures to sustain those customers has become urgent needs. Recently, classification models based on mathematical programming have been widely used in business intelligence. The purpose of this paper is to propose several multiple criteria programming methods for classification and apply these methods to VIP E-Mail behavior classification. We first introduce a model for a generalized multiple criteria programming based classification method, specifically four particular forms, and then we use a cross-validation method to test the stability and accuracy of multiple criteria programming methods on VIP E-Mail accounts. Finally, we compare our models with Support Vector Machine (SVM). The results show that the classification models based on mathematical programming are satisfactorily accurate and stable on a VIP E-Mail dataset. Therefore, it can be concluded that applying the proposed method on VIP E-Mail behavior analysis can provide stable and credible results. This research has been supported by the National Science Foundation of China (NSFC) under Grants No. 60674109, No. 70621001, and No. 70871111, and BHP Billiton Co., Australia. In the process of building the multiple criteria programming classification models, the authors received much help from Professor Juliang Zhang. We express our endless gratitude to him.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Web Intelligence and Agent Systems: An International Journal
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
