Two-step Variable Screening Method for the Mahalanobis-Taguchi Method with Small Training Data

This paper proposes a method that applies the Mahalanobis-Taguchi (MT) method in situations where the number of variables exceeds the number of training samples. In the proposed method, the diagnostic effectiveness of the variables is first evaluated using signal-to-noise (SN) ratio, and the MT method is then applied using only those variables with high diagnostic effectiveness. Accordingly, the variables are selected via a two-step process: (1) variables are selected before creating the unit space and (2) conventional variable selection is carried out using the MT method. The results of experimental evaluation of the effectiveness of the proposed method using four different datasets from the UCI Machine Learning Repository indicate that its diagnostic performance is superior to that of the RT method.