Data mining: concepts and techniques
Data mining: concepts and techniques
Solving a Class of Linearly Constrained Indefinite QuadraticProblems by D.C. Algorithms
Journal of Global Optimization
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 9 - Volume 9
Introduction To Business Data Mining
Introduction To Business Data Mining
WSIPL: An XML scripting language for integrating web service data and applications
Web Intelligence and Agent Systems
Web Intelligence and Agent Systems
Mining world knowledge for analysis of search engine content
Web Intelligence and Agent Systems
Encoding process algebraic descriptions of web services into BPEL
Web Intelligence and Agent Systems
An Operable Email Based Intelligent Personal Assistant
World Wide Web
Pattern recognition for MCNs using fuzzy linear programming
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
classifications of credit cardholder behavior by using multiple criteria non-linear programming
CASDMKM'04 Proceedings of the 2004 Chinese academy of sciences conference on Data Mining and Knowledge Management
Decision Rule Extraction for Regularized Multiple Criteria Linear Programming Model
International Journal of Data Warehousing and Mining
Qualitative preference-based service selection for multiple agents
Web Intelligence and Agent Systems
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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.