@article { author = {Cudney, Elizabeth A. and Hong, Jungeui and Jugulum, Rajesh and Paryani, Kioumars and Ragsdell, Kenneth M. and Taguchi, Genichi}, title = {An Evaluation of Mahalanobis-Taguchi System and Neural Network for Multivariate Pattern Recognition}, journal = {Journal of Industrial and Systems Engineering}, volume = {1}, number = {2}, pages = {139-150}, year = {2007}, publisher = {Iranian Institute of Industrial Engineering}, issn = {1735-8272}, eissn = {2717-3380}, doi = {}, abstract = {The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is to compare the ability of the Mahalanobis- Taguchi System and a neural-network to discriminate using small data sets. We examine the discriminant ability as a function of data set size using an application area where reliable data is publicly available. The study uses the Wisconsin Breast Cancer study with nine attributes and one class.}, keywords = {Mahalanobis-Taguchi System,Mahalanobis distance,Neural Network,Pattern recognition,Orthogonal array,Signal-to-noise ratio,Mahalanobis space (reference group)}, url = {https://www.jise.ir/article_3924.html}, eprint = {https://www.jise.ir/article_3924_5f31f4ac2ae98ac31c98b527e637819e.pdf} }