Article ID: | iaor19981906 |
Country: | Netherlands |
Volume: | 74 |
Issue: | 1 |
Start Page Number: | 89 |
End Page Number: | 112 |
Publication Date: | Nov 1997 |
Journal: | Annals of Operations Research |
Authors: | Stam Antonie, Asparoukhov Ognian K. |
Keywords: | programming: integer, statistics: multivariate |
In this paper, we introduce a nonparametric mathematical programming (MP) approach for solving the binary variable classification problem. In practice, there exists a substantial interest in the binary variable classification problem. For instance, medical diagnoses are often based on the presence or absence of relevant symptoms, and binary variable classification has long been used as a means to predict (diagnose) the nature of the medical condition of patients. Our research is motivated by the fact that none of the existing statistical methods for binary variable classification, parametric and nonparametric alike, are fully satisfactory. The general class of MP classification methods facilitates a geometric interpretation, and MP-based classification rules have intuitive appeal because of their potentially robust properties. These intuitive arguments appear to have merit, and a number of research studies have confirmed that MP methods can indeed yield effective classification rules under certain non-normal data conditions, for instance if the data set is outlier-contaminated or highly skewed. However, the MP-based approach in general lacks a probabilistic foundation, necessitating an