## 2 Related Work  
Methods for imbalanced data classification are typically categorized into two main types of approach: data-processing techniques and algorithm/model-oriented methods. These studies have laid a solid theoretical foundation for extending deep learning methods to imbalanced data classification. In terms of data-processing techniques, the main approaches are resampling and undersampling techniques [23; 24; 25]. These methods are designed to equalize the number of samples in different classes or to minimize noise, thereby improving the model's performance when dealing with imbalanced data. Undersampling eliminates data, thereby decreasing the total amount of information available for the model to learn. Conversely, oversampling boosts the number of minority class samples, which enlarges the training dataset and can lead to overfitting [26]. To address this issue, [PERSON] and [PERSON] [27] proposed using K-nearest neighbors (K-NN) to calculate the distances between the sample to be classified and the samples in the training set, selecting most samples for removal. [PERSON] et al. [8] introduced the SMOTE, which creates synthetic samples for the minority class by interpolating between existing minority class samples. Unlike traditional methods that replicate minority class samples, the SMOTE reduces the risk of overfitting and significantly enhances classifier performance on imbalanced datasets. In contrast to data-sampling methods, algorithmic approaches for addressing class imbalance do not modify the distribution of the training data. Instead, they adjust the learning or decision-making process, including cost-sensitive learning and ensemble learning. Cost-sensitive learning primarily involves adjusting learning algorithms based on the classification costs of different classes to effectively handle minority class samples [28]. In 2013, [PERSON] et al. proposed a cost-sensitive learning method based on Gradient-Boosting Decision Trees, effectively improving the accuracy and robustness of models when dealing with imbalanced data [29]. Additionally, research has explored ways to enhance cost-sensitive learning capabilities by integrating deep learning techniques, such as designing loss functions suitable for deep neural networks or hierarchical cost-sensitive mechanisms [30]. [PERSON] et al. [31] presented three cost-sensitive boosting techniques: AdaC1, AdaC2, and AdaC3. These methods introduce cost terms into the weight updates of AdaBoost, iteratively increasing the influence of the minority classes. [PERSON] demonstrated that, in most cases, cost-sensitive boosting ensemble methods outperformed ordinary boosting methods. [PERSON] et al. [32] combined cost-sensitive learning with Support Vector Machines (SVMs), employing a cost-sensitive SVM algorithm to adjust the margin and induce boundary drift, thereby improving the recall rate of minority classes. Ensemble learning is a method that improves predictive performance by combining multiple base classifiers. Early ensemble learning methods, such as Random Forest, which combines bagging and decision trees, emphasize the importance of feature randomness [33]. Gradient-Boosting Machines (GBMs) and their improved versions, such as XGBoost and LightGBM, significantly enhance prediction accuracy through optimized loss functions and enhanced collaboration among learners [34]. [PERSON] et al. [35] integrated data-level methods with ensemble learning, combining predictions from multiple CNN sub-models to enhance classification performance. Additionally, there are other methods at the algorithmic research level. For example, [PERSON] et al. [36] proposed a cumulative learning approach that decomposes class distributions into balanced subsets to address long-tail recognition issues. This method has demonstrated promising outcomes for managing highly imbalanced data.