This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Lee, E. K. Articles by Patterson, D. A. Search for Related Content

A Linear Programming Approach to Discriminant Analysis with a Reserved-Judgment Region

School of Industrial and Systems Engineering, Georgia Institute of Technology, 765 Ferst Drive, Atlanta, Georgia 30332-0205, USA and Department of Radiation Oncology, Emory University School of Medicine, 1365 Clifton Road, Atlanta, Georgia 30322, USA
Department of Medical Informatics, Columbia University, 622 West 168th Street—VC 5, New York, New York 10032, USA
Department of Mathematical Sciences, University of Montana, Missoula, Montana 59812-0864, USA
evakylee{at}isye.gatech.edu
gallric{at}dmi.columbia.edu
davep{at}selway.umt.edu

A linear-programming model is proposed for deriving discriminant rules that allow allocation of entities to a reserved-judgment region. The size of the reserved-judgment region, which can be controlled by varying parameters within the model, dictates the level of aggressiveness (cautiousness) of allocating (misallocating) entities to groups. Results of simulation experiments for various configurations of normal and contaminated normal three-group populations are reported for a variety of parameter selections. Results of cross-validation experiments using real data sets are also reported. Both the simulation and cross-validation experiments include comparison with other discriminant analysis techniques. The results demonstrate that the proposed model is useful for deriving discriminant rules that reduce the chances of misclassification, while maintaining a reasonable level of correct classification.

Key words: artificial intelligence; simulation; statistical analysis
History: received October 1998; revised January 2002; accepted January 2002.

This article has been cited by other articles:

				M. T. McCabe, E. K. Lee, and P. M. Vertino A Multifactorial Signature of DNA Sequence and Polycomb Binding Predicts Aberrant CpG Island Methylation Cancer Res., January 1, 2009; 69(1): 282 - 291. [Abstract] [Full Text] [PDF]