#### 6.1.2 Multiple-Type Statistically Weighted Dimension  
In the first experiment, the impact of a single statistically weighted dimension characteristic for both promoted and demoted sides is examined. This section of the experiment is performed on how well three different statistically weighted dimension feature approaches work together without PCA, and also along with PCA. Additionally, each method factor is investigated only for 125 (\(5\times 5\times 5\)) combinations in which the exponents vary between \(-1.0\) to \(+1.0\) with a step size of 0.5. This setting is made based on the exponent's survey from the first experiment in which the exponent values vary from \(-3\) to \(+3\) with a step size of 0.5 (see Table 2). Table 3 displays the best 20 (or best 10), and the worst 20 (or worst 10) numbers in which three classification algorithms, (1) logistic regression, (2) KNN, and (3) SVM (RBF), are surveyed. For example, in the first row of Panel 1.1, Panel A (best), the number 9(5) indicates that nine of the best 20 weightings and five of the best 10 weights have the exponent value of **ACSW**\(=-1\). However, Panel 1.1 displays the numbers for the best 20 (best 10 in parenthesis), and Panel B displays the worst 20 (worst 10 in parenthesis). The experiment is performed on three standard MNIST/E-MNIST/F-MNIST datasets using three different classification algorithms. The classification performance result values are shown in Table 3; which shows the numbers for the best 20 (best 10) and the worst 20 (worst 10). The experiment compares the performance between the statistically weighted dimension feature without PCA and the statistically weighted dimension feature based on PCA (contribution rate = 0.99). In Table 3 Panel A, most of the best 20 numbers (best 10 in parenthesis) depict that the **ICSW** method from our proposed combination of statistically weighted dimension features has 0 value on the demoting side with an exponent equal to \(-1\), \(-0.5\). Similarly, most of the best numbers on the promote side have an exponent value equal to \(+1\), \(+0.5\). It can be implied that the **ICSW** method for our proposed combination cases works well on the promoting side. In contrast, **ACSW**, and **SDW** have greater performance on the demoting side rather than on the promoting side, in which the most of best counting numbers appear on the demoting side. Similarly, the results of both statistically weighted dimension feature techniques working together without and with PCA indicate that the statistically weighted dimension feature is beneficial for guiding image classification. As a result, the best or worst numbers are chosen based on side dominance rather than typical weighted information (exponent equal to 0). It also can be concluded that the counting number of best or worst are selected either on the promoting or demoting side, and their values are more dominant than the traditional weighting information (exponent equal to 0). Furthermore, the effectiveness of the three image classification algorithms with the proposed scheme on three datasets show that KNN, logistic regression, and SVM (RBF) come in this order of effectiveness. For example, in both Panel 2.1 and Panel 2.2 for the best 20 (best 10 in parentheses), the performance of the KNN method shows that **ICSW** is on the promoting side, while **ACSW** and **SDW** are on the demoting side. Therefore, these results clearly reflect that KNN uses a degree of exponents higher than logistic regression and SVM (RBF). One possible cause may come from the effect of the proposed scheme; the image classification algorithm has high performance based on classifying the similarity concept. Furthermore, it can be concluded that the proposed approach on three image classification algorithms has high efficiency by using the high exponents for the MNIST, E-MNIST, and F-MNIST datasets, respectively. However, in the case of logistic regression for E-MNIST, it is concluded that it has an impact with a high exponent value rather than other two datasets.