# Classification Performance of Thresholding Methods in the Mahalanobis-Taguchi System
## 5 Results and Discussion
### Classification Accuracy Results
On the other hand, Box-Cox Transformation method seems superior on Ring and Sonar datasets while Type-I-Type-II error method was found best  
Figure 11: Frequency of successes over 20 datasets by all threshold methods. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Testing Dataset**} & \multicolumn{3}{c}{**Classification Accuracy (\%) Based on MD\({}_{\text{T}}\) Obtained Via**} \\ \multirow{2}{*}{**S.No**} & \multirow{2}{*}{**Dataset**} & **Optimum** & \multirow{2}{*}{**Normal**} & **Abnormal** & **TypeI-TypeII** & **ROC Curve** & **Chebyshev’s** & **Box-Cox** \\  & & & **Variables after** & **Samples** & **Samples** & **(\%)** & **(\%)** & **Theorem (\%)** & **Transformation** \\ \hline
1 & Appendicitis & 4 & 43 & 11 & **70.37** & **70.37** & 66.67 & **70.37** \\
2 & Banana & 2 & 1188 & 1462 & **68.49** & 62.11 & 44.91 & **68.49** \\
3 & Bupa & 5 & 100 & 73 & 50.87 & 55.49 & **57.23** & 50.87 \\
4 & Coil2000 & 48 & 4618 & 293 & 57.95 & 57.95 & **87.62** & 58.79 \\
5 & Haberman-2 & 3 & 113 & 41 & 65.58 & 65.58 & **74.68** & 61.69 \\
6 & Heart & 9 & 75 & 60 & 74.81 & 74.81 & **75.56** & 74.81 \\
7 & Ionosphere & 26 & 113 & 63 & **95.45** & **95.45** & 93.75 & **95.45** \\
8 & Magic & 9 & 6166 & 3344 & **74.13** & 73.14 & 75.87 & 74.17 \\
9 & Monk2 & 6 & 102 & 114 & **53.09** & 53.24 & 53.24 & 53.70 \\
10 & phoneme & 4 & 1099 & 793 & 62.84 & 65.06 & **72.13** & 62.36 \\
11 & Pima & 6 & 250 & 134 & 65.36 & 65.36 & **67.97** & 65.89 \\
12 & Ring & 20 & 1868 & 1832 & 60.05 & 74.95 & 92.41 & **98.14** \\
13 & Sonar & 58 & 46 & 49 & **52.63** & **52.63** & 51.58 & 51.58 \\
14 & Spambase & 28 & 1393 & 906 & **82.51** & **82.51** & 78.60 & **82.51** \\
15 & Spechtheart & 38 & 106 & 28 & **64.18** & 29.85 & 49.25 & 63.43 \\
16 & Titanic & 2 & 745 & 356 & **74.21** & **74.21** & **74.21** & **74.21** \\
17 & Wdbc & 13 & 179 & 106 & 91.23 & 91.23 & **92.63** & 91.23 \\
18 & Wisconsin & 6 & 222 & 120 & **96.20** & 90.63 & 90.31 & **96.20** \\
19 & Medical Diagnosis & & & & & & & \\  & of Liver Disease & & & & & & & \\  & Taguchi Character & & & & & & & \\  & Recognition & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Classification accuracy based on MD\({}_{\text{T}}\) suggested by each threshold method. on Monk2 and Spectfheart. These findings indicate that the suitability and utilization of each thresholding method depend on the dataset itself. Therefore, one could conduct a trial run for all thresholding methods to come to the decision in selecting the suitable thresholding method for any dataset of interest however, it seems impractical and will increase classification efforts. Further studies should be conducted to investigate the nature and the attributes of the datasets to which thresholding methods are suitable. Perhaps a systematic procedure could be developed to guide the decision process. Another interesting point to highlight is that out of 20 datasets, only seven of them (coil2000, Ionosphere, Ring, Spambase, Wolbc, Wisconsin, Medical Diagnosis of Liver Disease and Taguchi Character Recognition) produced classification accuracies of more than 80% across all thresholding methods by which an above 80% marks (>80%) is considered a promising prediction result [9]. The remaining datasets produced classification accuracy results with below than 80% of predictive accuracies across all thresholding methods. The lowest classification accuracy was seen on the Spectfheart dataset with a staggering low of 29.85% accuracy when predicting the testing samples based on the threshold value suggested by the ROC curve method. Generally, this not only denotes the unsuitability of the ROC method on the dataset, it also denotes that the predictive capability of the MTS seems unpromising in certain cases of datasets. This could be due to the validity of the reduced number of variables achieved during the optimization procedure of the MTS by which Orthogonal Array (OA) is utilized for feature selection.