Classification performance of thresholding methods in the Mahalanobis–Taguchi system - Faizir, Ramlie; Wan Zuki Azman, Wan Muhamad; Nolia, Harudin; Mohd Yazid, Abu; Haryanti, Yahaya; Khairur Rijal, Jamaludin; Hayati Habibah, Abdul Talib (MDPI, 2021)
The Mahalanobis–Taguchi System (MTS) is a pattern recognition tool employing Maha-lanobis Distance (MD) and Taguchi Robust Engineering philosophy to explore and exploit data in multidimensional systems. The MD metric provides a measurement scale to classify classes of samples (Abnormal vs. Normal) and gives an approach to measuring the level of severity between classes. An accurate classification result depends on a threshold value or a cut-off MD value that can effectively separate the two classes. Obtaining a reliable threshold value is very crucial. An inaccurate threshold value could lead to misclassification and eventually resulting in a misjudgment decision which in some cases caused fatal consequences. Thus, this paper compares the performance of the four most common thresholding methods reported in the literature in minimizing the misclas-sification problem of the MTS namely the Type I–Type II error method, the Probabilistic thresholding method, Receiver Operating Characteristics (ROC) curve method and the Box–Cox transformation method. The motivation of this work is to find the most appropriate thresholding method to be utilized in MTS methodology among the four common methods. The traditional way to obtain a threshold value in MTS is using Taguchi’s Quadratic Loss Function in which the threshold is obtained by minimizing the costs associated with misclassification decision. However, obtaining cost-related data is not easy since monetary related information is considered confidential in many cases. In this study, a total of 20 different datasets were used to evaluate the classification performances of the four different thresholding methods based on classification accuracy. The result indicates that none of the four thresholding methods outperformed one over the others in (if it is not for all) most of the datasets. Nevertheless, the study recommends the use of the Type I–Type II error method due to its less computational complexity as compared to the other three thresholding methods.