Several multi-criteria programming methods for classification

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Journal Article
Computers & Operations Research, 2009, 36 (3), pp. 823 - 836
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In this paper, we propose a general optimization-based model for classification. Then we show that some well-known optimization-based methods for classification, which were developed by Shi et al. [Data mining in credit card portfolio management: a multiple criteria decision making approac. In: Koksalan M, Zionts S, editors. Multiple criteria decision making in the new millennium. Berlin: Springer; 2001. p. 42736] and Freed and Glover [A linear programming approach to the discriminant problem. Decision Sciences 1981; 12: 6879; Simple but powerful goal programming models for discriminant problems. European Journal of Operational Research 1981; 7: 4460], are special cases of our model. Moreover, three new models, MCQP (multi-criteria indefinite quadratic programming), MCCQP (multi-criteria concave quadratic programming) and MCVQP (multi-criteria convex programming), are developed based on the general model. We also propose algorithms for MCQP and MCCQP, respectively. Then we apply these models to three real-life problems: credit card accounts, VIP mail-box and social endowment insurance classification. Extensive experiments are done to compare the efficiency of these methods.
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