Mathematical programming based heuristics for improving LP-generated classifiers for the multiclass supervised classification problem

Mathematical programming is used as a nonparametric approach to supervised classification. However, mathematical programming formulations that minimize the number of misclassifications on the design dataset suffer from computational difficulties. We present mathematical programming based heuristics for finding classifiers with a small number of misclassifications on the design dataset with multiple classes. The basic idea is to improve an LP-generated classifier with respect to the number of misclassifications on the design dataset. The heuristics are evaluated computationally on both simulated and real world datasets.