# Classification Performance of Thresholding Methods in the Mahalanobis-Taguchi System
## 5 Results and Discussion
### Classification Accuracy Results
MD values is sensitive to the choices of variables in the classifier system since the computed MD value varies with different sets of significant variables. Therefore, obtaining the optimal significant variable set is crucial in the MTS particularly on the MS (the reference group). Future studies should investigate the practicality of OA as an effective scheme for significant feature selection in the MTS. The suggestion seems to agree with reports in the literature claiming that the feature selection search mechanism using an orthogonal array (OA) for variable reduction in the MTS is inadequate and leads to inaccurate and sub-optimal solutions [41; 42; 43; 44; 45] for certain datasets. OA failed to explore other potential optimum combinations of features in their studies since the exploitation on higher-order combinations among variables in datasets using OA search structure was seen as insufficient. The use of Swarm Intelligence-based algorithms (SI) such as the Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Bees Algorithms (BA), Fish Algorithms (FA) to name a few, could be one alternative to handle the issue as suggested by [25]. SI-based algorithms are meta-heuristic in nature in that the search mechanism is tailored to guide a specific optimization problem heuristically toward promising solution search spaces that contain good quality solutions [46]. Further, the combination of exploration (diversification) and exploitation (intensification) search mechanism of the SI increases the ability to find optimal solutions in a reasonable time [47; 48]. Hence, the strategies offered by these algorithmic techniques are worth to be explored in solving the weakness of OA in that respect. Others also suggested several alternative methods replacing the OA in MTS such as the adaptive One-Factor-at-a-Time (aOFAT) [49] and Rough-set Theory [50]. Perhaps a modification of the OA matrix structure itself with other orthogonal matrix theories such as the [PERSON]'s cyclic matrix or Hadamard matrix [23] could possibly worth to be considered. In MTS, [PERSON] recommended using two types of signal-to-noise ratio which are \"larger-the-better\" and \"Dynamic\" signal-to-noise ratio. The former was utilized in this work. The latter type of signal-to-noise ratio (Dynamic) is another powerful selection metric that takes into account the level of abnormality of the input samples in its computational procedures. Unlike larger-the-better type signal-to-noise ratio, the Dynamic signal-to-noise ratio formulation is quite complex which makes the computational effort a challenging task however, it may provide a more promising solution. Thus, exploiting what the Dynamic signal-to-noise ratio could offer in improving the feature selection process of the MTS would be an encouraging research study in the future. Nonetheless, this study focuses on the comparison of thresholding classification performances in the MTS between the four threshold methods mentioned previously. Based on this study, it was clearly shown that not a single threshold method produced superior classification performance for all datasets. Nevertheless, the authors seem to recommend the use of the Type-I-Type-II error method as the alternative approach as compared to the other thresholding methods owing to its simplicity with less computational burden. However, it is suggested that more studies with more datasets could be conducted in the future to strongly support this generalization.