An additive utility mixed integer programming model for nonlinear discriminant analysis

J. Glen

Research output: Contribution to journalArticlepeer-review


Mathematical programming (MP) discriminant analysis models can be used to develop classification models for assigning observations of unknown class membership to one of a number of specified classes using values of a set of features associated with each observation. Since most MP discriminant analysis models generate linear discriminant functions, these MP models are generally used to develop linear classification models. Nonlinear classifiers may, however, have better classification performance than linear classifiers. In this paper, a mixed integer programming model is developed to generate nonlinear discriminant functions composed of monotone piecewise-linear marginal utility functions for each feature and the cut-off value for class membership. It is also shown that this model can be extended for feature selection. The performance of this new MP model for two-group discriminant analysis is compared with statistical discriminant analysis and other MP discriminant analysis models using a real problem and a number of simulated problem sets.

Original languageEnglish
Pages (from-to)1492-1505
Number of pages14
JournalJournal of the Operational Research Society
Issue number11
Early online date29 Aug 2007
Publication statusPublished - Nov 2008


  • mathematical programming
  • nonlinear classifiers
  • discriminant analysis


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