# Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
## 5 Results And Discussion  
### Classification Performance  
To ensure fairness in the experiments and minimize potential biases, each method was independently run 30 times on each dataset with different random seeds. The analysis of the experimental results primarily relies on the average classification accuracy and standard deviation on the test sets. Tables 13-17 list the average classification accuracy and standard \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Population size & 250 & Tree depth & 2-11 \\ Generations & 50 & Selection method & Tournament selection \\ Crossover rate & 0.8 & Tournament size & 7 \\ Mutation rate & 0.19 & Initialization method & Ramped half and half \\ Elitism rate & 0.01 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 12: GP parameters. deviation for ITACIE-GP and the comparison methods on the FEI_1, FEI_2, FLOWER, JAFFE, KTH, and ORL datasets in five scenarios. The non-parametric Wilcoxon Signed-Rank test assesses the significant differences between the methods, without assuming they follow a normal distribution. ITACIE-GP compares with competitors at a significance level of \(\alpha=0.05\), denoted in the W-test column. The symbols (+/=/-) respectively indicate that ITACIE-GP is better than/similar to/inferior to the comparison methods in terms of the benchmark datasets. The best classification results for each dataset are highlighted in bold and marked in red, the second-best results are marked in green, and the third-best results are marked in blue. The overall results of the significance tests are summarized in the last row. In 180 comparisons with SVM + Gabor, SVM + Histogram, SVM + HOG, SVM + SIFT, and SVM + DWT methods, the ITACIE-GP approach significantly outperformed the comparative methods in 178 comparisons and demonstrated similar performance in 2 comparisons. Only in original and low-contrast scenarios does SVM + SIFT perform similarly to ITACIE-GP on the ORL dataset. In other scenarios, ITACIE-GP holds a notable advantage. This indicates that the simple extraction of manual features followed by classification is ineffective, and performance degrades under the influence of various adverse scenarios. In contrast, benefiting from the adoption of a program structure based on information transmission, the ITACIE-GP method can evolve and construct effective features for accurate classification based on the characteristics of the classification problem. Moreover, it does not experience a significant decline in performance due to interference from adverse scenarios. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**Methods**} & **FEI\_3** & **W-Test** & **FEI\_2** & **W-Test** & **HOWER** & **W-Test** & **JJFFE** & **W-Test** & **KTH** & **W-Test** & **ORL** & **W-Test** \\  & **(Mean + Std)** & **(Mean + Std)** & **(Mean + Std  
In 60 comparisons with Lenet-5 and CNN-5, the ITACIE-GP method significantly outperformed the comparative methods in 59 comparisons and showed similar performance in 1 comparison. The ITACIE-GP method demonstrated superior performance on almost all datasets. This phenomenon may be attributed to the relatively simple architectures of Lenet-5 and CNN-5, while the ITACIE-GP method has the ability to evolve solutions of different scales based on the problem definition. The ITACIE-GP method demonstrated significant advantages in 150 comparisons with MIASNet, VGG, AlexNet, ResNet, and SqueezeNet. Specifically, in 136 comparisons, the ITACIE-GP method was significantly superior to the comparison method. In 5 comparisons, its performance was similar to the comparison method, while in 9 comparisons, its performance was inferior to the comparison method.