Imbalanced datasets often lead to biased classifiers and suboptimal generalization performance. To address this challenge, this study proposes a two-stage resampling hyperparameter prediction framework that automatically determines near-optimal resampling configurations based on dataset characteristics. In the training stage, a large collection of imbalanced datasets is compiled, from which self-features are extracted. Pre-sampling and classification are performed across a predefined hyperparameter space, and the resulting performance metrics—accuracy, sensitivity, and specificity—are used to train a Random Forest regression model capable of predicting continuous resampling hyperparameters. In the deployment stage, self-features from a new dataset are fed into the trained model to estimate suitable resampling parameters, which are subsequently used to balance the dataset and train the final classifier. Experimental results demonstrate that the proposed framework effectively reduces manual tuning effort while achieving competitive or improved classification performance compared to traditional resampling approaches. This work provides a practical and scalable solution for automated handling of imbalanced datasets.