## 4 Results Analysis  
### Evaluation Indicators  
To rigorously validate the effectiveness of the proposed PRI-SWT model and to clearly demonstrate its advantages over traditional methods such as SVM and ViT, a thorough  
Figure 8: The left side of the figure presents a schematic representation of the PRI-SWT model, detailing all its structures and operational processes. This includes key modules such as the separation layer, the patch embedding layer, the patch merging layer, the Swin Transformer module, and the fusion layer. The figure also provides information on the dimensions of the feature map tensors corresponding to the outputs of different modules. As the depth of the layers increases, the resolution of the feature maps gradually decreases, while the number of feature channels steadily increases. On the right side, the figure illustrates the internal details of the Swin Transformer module, which primarily comprises the window multi-head self-attention calculation module, the shifted window multi-head attention calculation module, the normalization layer, and the linear mapping MLP layer.  
Figure 9: The working mechanism of the Sliding Window Transformer. The image is segmented into numbered windows based on varying offset values and window sizes. These windows are then shifted and padded horizontally and vertically, forming structured regions for self-attention computation.  
performance evaluation is required. Establishing a set of comprehensive evaluation metrics is critical for ensuring a robust comparison. These indicators will not only quantify classification accuracy but also provide valuable insights into each model's specific strengths and limitations. The following key metrics have been identified to assess model performance:  
Confusion matrix: This matrix provides a detailed analysis of the model's classification performance across multiple categories. It reveals the level of confusion between categories and highlights the probability of misclassification, offering valuable insights into areas where the model may struggle.  
Top-1 accuracy and top-2 accuracy: These metrics evaluate accuracy by determining whether the correct classification result appears within the first- or second-ranked categories in the probability list produced by each classification node. These metrics are crucial for understanding the model's ability to deliver correct classifications.  
We used the following macro indicators:  
Precision: defined as the ratio of the diagonal element in the confusion matrix to the sum of the elements in the corresponding row, precision indicates how many of the selected instances are relevant.  
Recall: calculated as the sum of the elements in the corresponding column, recall measures the model's ability to identify all relevant instances.  
Macro-P:  
\[macro\_P=\frac{1}{n}\sum\limits_{i=1}^{n}P_{i} \tag{5}\]  
Macro-R:  
\[macro\_R=\frac{1}{n}\sum\limits_{i=1}^{n}R_{i} \tag{6}\]  
where \(P_{i}\) and \(R_{i}\) are the precision and recall values for each category, respectively, and \(n\) is the number of categories.  
Macro-F1:  
\[macro\_F1=\frac{2}{\frac{1}{macro\_P}+\frac{1}{macro\_R}} \tag{7}\]  
This metric provides the harmonic mean value of precision and recall, offering a single score that captures both aspects of performance. It balances the trade-off between precision and recall, making it a valuable measure of overall classification effectiveness.  
Kappa coefficient: This metric assesses the consistency of classification outcomes. Higher Kappa values typically indicate greater accuracy and reliability regarding the model's predictions. The Kappa coefficient is calculated as follows:  
\[kappa=\frac{p_{0}-p_{e}}{1-p_{e}},p_{0}=\frac{\sum\limits_{i=1}^{n}h_{ii}}{ \sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}h_{ij}},p_{e}=\frac{\sum\limits_{i= 1}^{n}\left(\sum\limits_{j=1}^{n}h_{ij}\right)^{2}}{\left(\sum\limits_{i=1}^{n }\sum\limits_{j=1}^{n}h_{ij}\right)^{2}} \tag{8}\]  
where \(p_{0}\) represents the observed agreement, and \(p_{e}\) represents the expected agreement by chance.