Resampling Strategy

Resampling strategies are data preprocessing techniques used to address class imbalances in datasets, improving the performance of machine learning models, particularly in classification tasks. Current research focuses on developing efficient algorithms, such as hybrid oversampling and undersampling methods, and adapting resampling to handle large datasets and time-series data, often incorporating online processing for real-time applications. These advancements are crucial for enhancing the reliability and accuracy of predictive models across diverse fields, from medical diagnosis to cybersecurity, where imbalanced data is common.