Several multi-criteria programming methods for classification
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 millenium. Berlin : Springer: 2001. p. 427-36] and Freed and G I over [A linear programming approach tot tic discriminant problem. Decision Sciences 1981: 12: 68-79: Simple but powerful goal programming models for discriminant problems. European Journal of Operational Research 1981: 7: 44-60]. 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. (C) 2007 Elsevier Ltd. All rights reserved.
classification data mining nonlinear programming multiple-criteria decision making