Error Distribution Smoothing:Advancing Low-Dimensional Imbalanced Regression
This addresses imbalanced data challenges in regression tasks for domains like finance or healthcare, but it appears incremental as it builds on existing imbalance concepts.
The paper tackles imbalanced regression problems in real-world datasets by introducing a novel concept of Imbalanced Regression and proposing Error Distribution Smoothing (EDS) to select a representative subset, showing effectiveness in experiments.
In real-world regression tasks, datasets frequently exhibit imbalanced distributions, characterized by a scarcity of data in high-complexity regions and an abundance in low-complexity areas. This imbalance presents significant challenges for existing classification methods with clear class boundaries, while highlighting a scarcity of approaches specifically designed for imbalanced regression problems. To better address these issues, we introduce a novel concept of Imbalanced Regression, which takes into account both the complexity of the problem and the density of data points, extending beyond traditional definitions that focus only on data density. Furthermore, we propose Error Distribution Smoothing (EDS) as a solution to tackle imbalanced regression, effectively selecting a representative subset from the dataset to reduce redundancy while maintaining balance and representativeness. Through several experiments, EDS has shown its effectiveness, and the related code and dataset can be accessed at https://anonymous.4open.science/r/Error-Distribution-Smoothing-762F.