Article ID: | iaor20021426 |
Country: | United Kingdom |
Volume: | 52 |
Issue: | 8 |
Start Page Number: | 896 |
End Page Number: | 904 |
Publication Date: | Aug 2001 |
Journal: | Journal of the Operational Research Society |
Authors: | Loucopoulos Constantine, Pavur R. |
Keywords: | programming: integer, statistics: multivariate |
This study examines the impact that the size of the classification gap can have on the classificatory performance of a mathematical programming based discriminant model. In mathematical programming based models that project the discriminant scores onto a line, the discriminant score of an observation may fall into the gap between adjacent group intervals; thus there is no clear cut way to determine the group in which the observation should be classified. We examine a procedure that we refer to as the split gap approach. The split gap approach is defined as a strategy of estimating the performance of a mathematical programming based model using a nonzero gap size to separate group intervals and then splitting the gap between adjacent group intervals to classify future observations. Studies that propose models with a classification gap generally do not assess the effect of the gap on the performance of the model. This paper investigates this effect. A theoretical assessment and a Monte Carlo simulation are used to determine the impact of different gap sizes on a mixed integer programming model using a single function classification model for the three-group case.