###### Abstract  
Classifying imbalanced data is important due to the significant practical value of accurately categorizing minority class samples, garnering considerable interest in many scientific domains. This study primarily uses analysis of variance (ANOVA) to investigate the main and interaction effects of different parameters on imbalanced data, aiming to optimize convolutional neural network (CNN) parameters to improve minority class sample recognition. The CIFAR-10 and Fashion-MNIST datasets are used to extract samples with imbalance ratios of 25:1, 15:1, and 1:1. To thoroughly assess model performance on imbalanced data, we employ various evaluation metrics, such as accuracy, recall, F1 score, P-mean, and G-mean. In highly imbalanced datasets, optimizing the learning rate significantly affects all performance metrics. The interaction between the learning rate and kernel size significantly impacts minority class samples in moderately imbalanced datasets. Through parameter optimization, the accuracy of the CNN model on the 25:1 highly imbalanced CIFAR-10 and Fashion-MNIST datasets improves by 14.20% and 5.19% compared to the default model and by 8.21% and 3.87% compared to the undersampling model, respectively, while also enhancing other evaluation metrics for minority classes. imbalanced data; analysis of variance; parameter optimization; fractional factorial design +
Footnote †: journal: Medical  
classifiers into a strong classifier, effectively improving the model's recognition ability for minority class samples. The performance and accuracy of CNNs are primarily influenced by network architecture and parameter selection. The LeNet-5 network architecture, developed by [PERSON] et al., marked the beginning of deep learning research [15]. Subsequently, the well-known AlexNet architecture, introduced by [PERSON] et al., further advanced the field of deep learning [1]. The VGG network, created by [PERSON] and [PERSON], and the ResNet network developed by [PERSON] et al. are among the most widely used and best-performing networks to date [16; 17]. In terms of parameter selection, researchers have made significant efforts. However, parameter selection for imbalanced data has not been extensively studied. This is primarily because there are many parameters that affect the classification performance of imbalanced data, and many of these parameters interact with each other. For example, learning rate, batch size, and regularization parameters can all have a significant impact on model performance [18]. Moreover, there is a diversity in the evaluation metrics for classification performance on imbalanced data. Unlike balanced data, metrics such as accuracy, ROC, and AUC do not comprehensively assess the performance of imbalanced classification problems [8; 19]. In practical scenarios, metrics like recall for the minority class, G-mean, and F1 score might be of greater concern. For example, in disease diagnosis, where the number of patients with the disease can be small, whether 100% of the diseased individuals can be identified is often of utmost importance to doctors. In the face of the aforementioned complex situations, simple parameter-tuning methods often cannot meet the needs of parameter selection for imbalanced samples. Therefore, this paper proposes an experimental design and parameter selection method based on factor analysis, which systematically analyzes the impact of parameters on model performance. Fractional factorial design [20] is a widely used statistical method in experimental design that is primarily employed to evaluate the impact of multiple factors and their interactions on experimental outcomes. By systematically varying the levels of multiple factors to study their effects on the response variable, fractional factorial design can effectively identify the main effects and interactions of each factor. The references provide a comprehensive and systematic approach for model parameter optimization [21; 22].