## 4 Machine Learning Model
### Parameter Determination  
The Radial Basis Function Kernel (RBF) is chosen for modeling. The RBF kernel function can map the original feature space to a high-dimensional feature space, enabling the data to be linearly separable in the new feature space, thereby addressing the issue of linear inseparability in the original feature space [50; 51]. A total of 80% of the data are utilized as the training set, and 20% of the data are used as the test set. The model quality is evaluated through 5-fold cross-validation, and the grid search method is employed to determine the optimal C and \(\gamma\).  
The search range for C and \(\gamma\) is [0.01, 100], and 50 candidate values are generated through linear space sampling, resulting in a total of 25,000 combinations. To evaluate the performance of the proposed model comprehensively, the area under the receiver operating characteristic curve (AUC) is employed to assess the performance of the multi-class SVM model. By using this approach, the classification ability of the model on different categories can be fully understood, and the accuracy and reliability of the evaluation results can be guaranteed. The AUC threshold is set at 0.7, and based on this, the SVM classification model  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Input Layer**} & \multicolumn{3}{c}{**Output Layer**} \\ \hline \multicolumn{2}{c}{**Context and Features**} & \multicolumn{3}{c}{**Average Amplitude of ERP/\(\mu\)V**} & \multicolumn{1}{c}{**Output Layer**} \\ \cline{2-5} \multicolumn{2}{c}{} & \multicolumn{1}{c}{**Channel**} & \multicolumn{1}{c}{**Time Window/ms**} & \\ \hline Context & Genuine/Fake & FP1, FPZ, FP2 & 200–1000 & \\ \hline Composition & Blank Space & PZ, POZ & 50–120 & \\ \hline Tone & Gray Histogram & OZ & 200–300 & \\ \hline \multirow{3}{*}{Global Texture} & Gabor-Mean & P7, PO3, PO5, PO7 & 70–130 & \\ \cline{2-5}  & Gabor-Variance & PZ, POZ & 70–130 & \\ \cline{2-5}  & Gabor-Energy & P2, P4, PZ, PO4, PO6, POZ & 70–130 & \\ \hline \multirow{3}{*}{Local Texture} & Horizontal GLCM & OZ & 500–1000 & \\ \cline{2-5}  & Diagonal GLCM & PO5, PO7 & 70–140 & \\ \cline{1-1} \cline{2-5}  & OZ & 300–1000 & \\ \cline{1-1} \cline{2-5}  & LBP & AF3, P5, FP1, FPZ & 300–1000 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of input and output layer data.  
with the highest ACC is sought to ensure that the model possesses strong discriminatory power while maximizing its overall classification accuracy.  
It can be seen from Figure 14 and Table 3 that the optimal solution has an average loss of 0.20199 in the cross-validation, an accuracy rate of 0.76866 on the test set, and an AUC of 0.74155. The performance of the model in different folds is relatively stable, and the performance on the training and validation sets is consistent, indicating that the model has suitable generalization ability.  
In contrast, by constructing the same SVM model using only image feature data, it was found that the optimal (C, \(\gamma\)) combination was (46.9441, 14.2943), leading to an accuracy of only 0.68657 on the test set. This shows that incorporating EEG data from the subjects and context data greatly enhances the model's accuracy. Relying solely on image feature data mainly focuses on the commonality of beauty (biological beauty) and neglects individual differences and the influence of the aesthetic context in the evaluation process.