Classification Performance of Thresholding Methods in the Mahalanobis–Taguchi System

Abstract

The Mahalanobis–Taguchi System (MTS) is a pattern recognition tool employing Mahalanobis 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 misclassification 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.

A Modified Mahalanobis Distance Metric Used to Optimize Mahalanobis Space and Improve Classification Performance of MTS

“…Jin and Chow performed Box-Cox transformation on MD and used three times the standard deviation of the transformed normal distribution to set the threshold and applied it to the motor fault diagnosis of cooling fans [22]. Ramlie et al compared the performance of the four most common thresholding methods, namely, the Type I-Type II error method, the probabilistic thresholding method, the receiver operating characteristics (ROC) curve, and the Box-Cox transformation method, in 20 different datasets and recommended the use of the Type I-Type II error method due to its lower computational complexity [23].…”

Application of the Mahalanobis-Taguchi System in Renal Profile of the Methadone Flexi Dispensing Program

“…Saad et al [39] developed an MTS-based graphical user interface to analyze and classify normal and abnormal patients under MFlex service for a better monitoring system. Ramlie et al [40] concluded that none of the four thresholding methods outperformed one over the others in (if it is not for all) most of the datasets. Harudin et al [41] proved that incorporating Bitwise Artificial Bee Colony (BitABC) techniques into Taguchi's T-Method methodology effectively improved prediction accuracy.…”

Methadone Flexi Dispensing (MFlex) Intelligence System utilizing the Mahalanobis-Taguchi System

“…[37] used the MFlex service to make the tracking system better by creating an MTS-based graphical user interface for analyzing and sorting patients into groups of normal and abnormal variants. According to [38], none of the four thresholding approaches outperformed the others in almost all of the datasets. [39] showed that combining Bitwise Artificial Bee Colony (BitABC) methods along with Taguchi's T-Method greatly improved the accuracy of predictions.…”