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Effect of prior probabilities on the classificatory performance of parametric and mathematical programming approaches to the two-group discriminant problem. (English) Zbl 0894.90174
Summary: A mixed-integer programming model (MIP) incorporating prior probabilities for the two-group discriminant problem is presented. Its classificatory performance is compared against that of Fisher’s linear discriminant function (LDF) and Smith’s quadratic discriminant function (QDF) for simulated data from normal and nonnormal populations for different settings of the prior probabilities of group membership. The proposed model is shown to outperform both LDF and QDF for most settings of the prior probabilities when the data are generated from nonnormal populations but underperforms the parametric models for data generated from normal populations.
MSC:
90C90 Applications of mathematical programming
62H30 Classification and discrimination; cluster analysis (statistical aspects)
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