A theoretical survey on Mahalanobis-Taguchi system

Abstract

The Mahalanobis-Taguchi System (MTS) is a diagnosis and forecasting method employing Mahalanobis Distance (MD) and Taguchi’s Robust Engineering in a multidimensional system. In MTS, MD is used to construct a continuous measurement scale to discriminate observations and measure the level of abnormality of abnormal observations which compared to a group of normal observations. Therefore, MTS can handle the class imbalance problem. In addition, MTS is unique in its robustness to assess variability among all the levels of observations (noise) and evaluate significant and insignificant features which contributed to the multidimensional system by means of simplistic yet robust technique via Orthogonal Arrays (OA) and Signal-to-Noise Ratios (SNR). However, compared with the classic multivariate methods, MTS has a weaker theoretical basis. In order to promote the development and improvement of MTS theory, this paper reviews the literature related to developing and improving MTS theory. The survey presents and analyzes the research results in terms of MD, SNR, Mahalanobis Space (MS), feature selection, threshold, multi-class MTS, and comparison with other methods. Finally, a detailed analysis of the future possible research directions will be proposed to develop and improve MTS.