## 7 Discussion  
This section describes statistically weighted features that have been investigated and utilized to guide feature selection based on class distribution characteristics, as well as related studies to improve image classification using lower dimension technique (PCA). Most developing PCA approaches employed relative information as a guideline to conduct feature extraction before starting the dimensional reduction procedure. Nevertheless, no research has been carried out on feature extraction as a class characteristic for feature weighting-based dimensional reduction in image classification. In the past, pixel values were used as a feature weight to determine feature components in the reduced dimension procedure. However, some recent work has straightforwardly applied relevant information in the form of statistics such as probability [8], entropy [16], and distribution [9], due to the fact that these statistics are based on the entire collection and are not affected by the inter- and intra-class properties. To address this limitation, some works [15; 17; 18] in the feature selection field only focus on class-based statistics to reflect the class characteristics  
Figure 2: Class-based measurement using accuracy and the number of components. Here, the circle marks indicate the performance when the contribution rate is used. (**a**) Accuracy of MNIST. (**b**) The component of MNIST. (**c**) Accuracy of E-MNIST. (**d**) The component of E-MNIST. (**e**) Accuracy of F-MNIST. (**f**) The component of F-MNIST.  
during classification. Our proposed improved image classification method uses statistically weighted dimensions as distribution class information to enhance the dimension reduction where the feature selection is PCA. It is noteworthy that this improvement in classification with statistically weighted dimensions on dimensionality reduction is quite significant. The class information affects the image classification process, as shown in Table 4. The statistically weighted dimension with PCA (SWD-PCA) is used in three image classifications, (1) logistic regression, (2) KNN, and (3) SVM (RBF). The best combination of SWD-PCA works well on KNN, which improves nearly 3% (varying 0.44% of MNIST, 2.84% of E-MNIST, and 1.17% of F-MNIST). The best SWD-PCA for the remaining two classification algorithms have improved by around 1% for logistic regression, varying by 0.08% for MNIST, 0.92% for E-MNIST, and 0.43% for F-MNIST, and for SVM (RBF) varying by 0.05% of MNIST, 0.84% for E-MNIST, and 0.81% for F-MNIST. The optimum combination decreased the number of components by more than 50%, with the average value decreasing from BASE-LINE for PCA with BASE-LINE weighting (3, see Table 4), and PCA with optimal statistically weighted dimension (4, see Table 4) for 55.74% (varying for 35.07% for SVM (RBF) on F-MNIST to 70.41% for KNN on E-MNIST), and 52.42% (varying from 34.82% for SVM (RBF) on F-MNIST to 70.53% for SVM (RBF) on MNIST). Moreover, the exponent direction for the SWD-PCA combination is similar to that observed in the text mining area [18], where most results show that ICSW performs well on the promoted side (+), while ACSW and SDW perform well on the degraded side (-). Figure 2 depicts the results that all SWD-PCA methods are superior to PCA types when the contribution rates are varied. This figure especially indicates that all classification techniques that combine statistical weighting with PCA (SWD-PCA) significantly enhance the performance over PCA. The high performance contributes at a rate of roughly 90% to 95%, with the remaining components ranging between 100 and 200 out of 784 (reducing the dimension around 75% to 87%).