AWSSS: Adaptive Weighted Statistical Space Smoothing for Regression with Imbalance Data

Deep learning models, usually trained on large datasets, have witnessed great achievements for both classification and regression tasks during past years. However, in practical settings, the datasets are commonly imbalanced where the number of data samples differs among labels (i.e.,categories or target values), leading to serious performance degradation of the trained models. Although many prior works have been proposed to solve the data imbalance problem for the classification task, there are still limited works considering the regression task. In this work, we identify the distinct challenges in regression problems compared to traditional classification problems, such as managing the continuous nature of the target variable and navigating fuzzy decision-making boundaries. To address these challenges, we propose an Adaptive Weighted Statistical Space Smoothing method (AWSSS), which alleviates the impact of imbalanced data by learning from continuously valued, imbalanced data and extending local similarity across the entire target range. AWSSS extracts statistical values from the original dataset and smoothes statistical features. Subsequently, it aligns features across different regions and applies adaptive weights to facilitate knowledge transfer between adjacent areas. Finally, AWSSS calculates the loss values, which are used to refine the model’s parameters through iterative updates. Extensive experiments conducted on various datasets demonstrate the effectiveness of the proposed method as compared to state-of-the-art methods.