A MIXED INTEGER PROGRAMMING MODEL FOR MULTIPLE-CLASS DISCRIMINANT ANALYSIS

Minghe Sun ()
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Minghe Sun: Department of Management Science and Statistics, College of Business, The University of Texas at San Antonio, San Antonio, TX 78249-0632, USA

International Journal of Information Technology & Decision Making (IJITDM), 2011, vol. 10, issue 04, 589-612

Abstract: A mixed integer programming model is proposed for multiple-class discriminant and classification analysis. When multiple discriminant functions, one for each class, are constructed with the mixed integer programming model, the number of misclassified observations in the sample is minimized. This model is an extension of the linear programming models for multiple-class discriminant analysis but may be considered as a generalization of mixed integer programming formulations for two-class classification analysis. Properties of the model are studied. The model is immune from any difficulties of many mathematical programming formulations for two-class classification analysis, such as nonexistence of optimal solutions, improper solutions, and instability under linear data transformation. In addition, meaningful discriminant functions can be generated under conditions where other techniques fail. Examples are provided. Results on publically accessible datasets show that this model is very effective in generating powerful discriminant functions.

Keywords: Discriminant analysis; classification; mixed integer programming; optimization; nonparametric procedures (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (2)

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DOI: 10.1142/S0219622011004476

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