An efficient Mahalanobis-Taguchi system for nonlinear multi-class classification problem
by Vinay Kumar; Sasadhar Bera; Indrajit Mukherjee; Anuradha Sarkar
International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 14, No. 3, 2022

Abstract: Mahalanobis-Taguchi system (MTS) is a robust predictive analytics technique used for diagnostic, forecasting, nonlinear classification, and feature selection of multivariate systems. MTS concept is applied in fault detection, medical diagnostic, and health hazards. However, most of the work on MTS addressed binary or two-class classification problems. There seems to be a lack of research that illustrates suitability and compares MTS performance for various nonlinear multi-class classification problems. This work's primary objective is to propose an efficient MTS (e-MTS) for nonlinear multi-class classification problems. A new approach to defining threshold Mahalanobis distance (MD) is also suggested to improve the classification performance of MTS. The secondary aim of this research is to illustrate the efficiency of e-MTS and compare its performance with back-propagation artificial neural network (BPNN) and radial basis function (RBF) kernel-based support vector machine (SVM). Analysis of various cases confirms the suitability of e-MTS for multi-class classification.

Online publication date: Mon, 16-Jan-2023

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