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A Comparison of Linear Programming and Parametric Approaches to the Two‐Group Discriminant Problem *
Author(s) -
Rubin Paul A.
Publication year - 1990
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1990.tb01691.x
Subject(s) - linear discriminant analysis , optimal discriminant analysis , discriminant , discriminant function analysis , linear programming , parametric statistics , kernel fisher discriminant analysis , benchmark (surveying) , mathematics , multiple discriminant analysis , computer science , artificial intelligence , machine learning , statistics , mathematical optimization , pattern recognition (psychology) , geodesy , facial recognition system , geography
Recent simulation‐based studies of linear programming models for discriminant analysis have used the Fisher linear discriminant function as the benchmark for parametric methods. This article reports experimental evidence which suggests that, while some linear programming models may match or even exceed the Fisher approach in classification accuracy, none of the fifteen models tested is as accurate on normally distributed data as the Smith quadratic discriminant function. At the minimum, further testing is warranted with an emphasis on data sets that arise from significantly non‐Gaussian populations.