My research in imbalance learning focuses on tackling the challenges posed by imbalanced datasets in machine learning. This field explores algorithmic adaptations, such as specialized algorithms and ensemble methods, to handle skewed class distributions. Sampling techniques like oversampling, undersampling, and generating synthetic samples are investigated to rebalance datasets. Imbalance learning has practical applications in fraud detection, medical diagnosis, and other scenarios with rare positive instances, aiming to enhance model generalizability and robustness in real-world settings.