### Classification Performance with Statistically Weighted Dimension and Dimensionality Reduction (PCA)
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Dataset** (**NO.)} & \multirow{2}{*}{**Features**} & \multicolumn{6}{c}{**Methods**} \\ \cline{3-10}  & & & \multicolumn{3}{c}{**LogisticRegression**} & \multicolumn{3}{c}{**KNN**} & \multicolumn{3}{c}{**SVM (RBF)**} \\  & & & **ACC (comp.)** & **(score, low, std)** & **ACC (comp.)** & **(score, low, std)** & **ACC (comp.)** & **(score, low, std)** \\ \hline
**Mnist** & & & & & & & & & & \\ (1) & BASE–LINE & 92.55 (784) & (0, 0, 0) & 96.65 (784) & (0, 0, 0) & 97.92 (784) & (0, 0, 0) \\ (2) & Optimal SWD & 92.71 (784) & (–1, 1, -1) & 97.05 (784) & (–0.5, 0.5, 0.5) & 98.02 (784) & (–0.5, 0.5, -1) \\ (3) & (1) + PCA & 92.13 (331) & (0, 0, 0) & 96.60 (331) & (0, 0, 0) & 98.27 (246) & (0, 0, 0) \\ (4) & (2) + PCA & 92.63 (432) & (–1, 1, -1) & 97.08 (269) & (–1, 0.5, 1) & 98.32 (231) & (–1, 0.5, -0.5) \\ \hline
**E–MNIST** & & & & & & & & & \\ (1) & BASE–LINE & 71.59 (784) & (0, 0, 0) & 83.82 (784) & (0, 0, 0) & 90.67 (784) & (0, 0, 0) \\ (2) & Optimal SWD & 72.33 (784) & (–1, 0.5, -1) & 86.29 (784) & (0, 0, 0) & 98.08 (784) & (0, 5, 0, -0.5) & 98.08 (784) & (0, 5, -0.5) \\ (3) & (1) + PCA & 71.97 (78) & (0, 0, 0) & 86.00 (208) & (0, 0, 0) & 91.94 (278) & (0, 0, 0) \\ (4) & (2) + PCA & 72.44 (416) & (–0.5, 0.5, -1) & 86.45 (247) & (0, 1, -1) & 91.45 (279) & (–0.5, 0, 0.5) \\ \hline
**F–MNIST** & & & & & & & & & \\ (1) & BASE–LINE & 85.44 (784) & (0, 0, 0) & 85.73 (784) & (0, 0, 0) & 89.21 (784) & (0, 0, 0) \\ (2) & Optimal SWD & 85.95 (784) & (–0.5, 0, -1) & 86.45 (784) & (–1, 0.5, -0.5) & 89.63 (784) & (–1, 0, 0.5) \\ (3) & (1) + PCA & 85.10 (459) & (0, 0, 0) & 85.83 (459) & (0, 0, 0) & 89.68 (509) & (0, 0, 0) \\ (4) & (2) + PCA & 85.81 (465) & (–1, 0.5, -1) & 86.73 (507) & (–1, 0.5, -0.5) & 89.93 (511) & (–0.5, 0, 0) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The comparison accuracy of statistically weighted dimension feature based on PCA analysis. The value before the parentheses is accuracy of classification, the value in first parentheses is the number of components, and the value in last parentheses is the number of exponents for **ACSW**, **ICSW**, and **SDW**. 34.82% for SVM (RBF) on F-MNIST to 70.53% for SVM (RBF) on MNIST). These results demonstrate that using PCA is more accurate than not using PCA. It appears to choose the feature based on the most important characteristics, which indicate/reflect the quality of the data and enhance the efficiency of image classification. Moreover, the statistically weighted dimension feature can cooperate with PCA for feature selection by enhancing the effectiveness of the identifier in the feature. One more important observation is all the best PCA results are obtained by using weighting values, which are in the same direction as the experiments in Table 4. It can be concluded that **ICSW** performs good weighting in the promoting direction, while most of **ACSW** and **SDW** perform good weighting in the demoting direction. Finally, the results that are obtained from the dataset used for feature selection with PCA clearly illustrate that the whole trend is in the same or a similar direction. However, in some cases, it is necessary to increase the value of the exponent with a larger lead than the case without PCA. For example, in panel MNIST, KNN Section (2, 4, see Table 4), the accuracy value is increased 0.031% by reducing the component 65.59%, which is guided by raising **ACSW** and **SDW** with one step (i.e., \(-0.5\)).