Title: Mahalanobis Taguchi System (MTS) and Mahalanobis Taguchi Gram-Schmidt (MTGS) methods as multivariate classification tools

Authors: Smarajit Bose; Rita SahaRay; Rohosen Bandyopadhyay

Addresses: Applied Statistics Division, Indian Statistical Institute, 203 B.T. Road, Kolkata-700 108, India ' Applied Statistics Division, Indian Statistical Institute, 203 B.T. Road, Kolkata-700 108, India ' Statistics Department, University of California, Davis, California, 95616, USA

Abstract: The Mahalanobis Taguchi System (MTS) and Mahalanobis Taguchi Gram-Schimdt (MTGS) methods were developed as diagnostic and predictive tools to separate between 'normal' and 'abnormal' data. The objective of these methods is to establish a measurement scale based on the 'normal' data so that the 'abnormal' data can be identified along with the degree of 'abnormality'. The goal of the present paper is to employ these methodologies as classification tools for multivariate data in general multi-class problems and compare the accuracy of the proposed tool with that of other existing multivariate classifiers using a variety of real life datasets.

Keywords: Mahalanobis-Taguchi system; MTS; Mahalanobis Taguchi Gram-Schimdt process; MTGS; Mahalanobis distance; Mahalanobis space; Taguchi methods; signal-to-noise ratio; S-N ratio; orthogonal arrays; classification tools; multivariate data.

DOI: 10.1504/IJISE.2014.057945

International Journal of Industrial and Systems Engineering, 2014 Vol.16 No.1, pp.102 - 119

Available online: 28 Oct 2013 *

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