Impact of Mahalanobis space construction on effectiveness of Mahalanobis-Taguchi system

Mahalanobis-Taguchi system (MTS) is a pattern recognition technique that aids in quantitative decisions by constructing a multivariate measurement scale using data analytic methods. In this paper, the importance of constructing the Mahalanobis space (MS) is demonstrated using the data from Soylemezoglu et al. (2010). The data collected from ten attributes for normal observations are treated using a control chart approach, similar to statistical process control models. Two MS models are constructed using the data inside the control limits of ±3σ and ±2σ for each variable and benchmarked in terms of accuracy, sensitivity, specificity and relative sensitivity. In addition, the impact of attribute selection is also demonstrated. This study shows that (1) a reliable MS is important for effective deployment of MTS; (2) the construction of MS, as well as selection of variables, should be driven by domain experts since understanding data in order to determine the normal observations require in-depth knowledge in the particular field of application and (3) for novice practitioners, filtering normal data using different control limits, applying MTS using alternative MS models, and investigating different combinations of significant features for the same application, and then determining the best MS model can be more effective.