Constrained global optimization: algorithms and applications
Constrained global optimization: algorithms and applications
Machine Learning
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
Mathematical Programming for Data Mining: Formulations and Challenges
INFORMS Journal on Computing
Summary from the KDD-03 panel: data mining: the next 10 years
ACM SIGKDD Explorations Newsletter
Introduction To Business Data Mining
Introduction To Business Data Mining
Modeling of mechanical properties and bond relationship using data mining process
Advances in Engineering Software
Assessing scorecard performance: A literature review and classification
Expert Systems with Applications: An International Journal
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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. 427-36] and Freed and Glover [A linear programming approach to the 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.