Constrained discriminant analysis via 0/1 mixed integer programming

A nonlinear 0/1 mixed integer programming model is presented for a constrained discriminant analysis problem. The model places restrictions on the numbers of misclassifications allowed among the training entities, and incorporates a ''reserved judgment'' region to which entities whose classifications are difficult to determine may be allocated. Two linearizations of the model are given - one heuristic and one exact. Numerical results from real-world machine-learning datasets are presented.