Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
482843 | European Journal of Operational Research | 2006 | 19 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Jan Adem, Willy Gochet,