Learning Confidence Bounds for Classification with Imbalanced Data
This addresses the challenge of biased models and unreliable predictions in imbalanced data for practitioners in classification tasks, though it appears incremental as it builds on existing methods like undersampling and oversampling.
The paper tackles the problem of class imbalance in classification by proposing a framework that uses learning theory and concentration inequalities to embed class-dependent confidence bounds into the learning process, resulting in more robust and reliable classification outcomes.
Class imbalance poses a significant challenge in classification tasks, where traditional approaches often lead to biased models and unreliable predictions. Undersampling and oversampling techniques have been commonly employed to address this issue, yet they suffer from inherent limitations stemming from their simplistic approach such as loss of information and additional biases respectively. In this paper, we propose a novel framework that leverages learning theory and concentration inequalities to overcome the shortcomings of traditional solutions. We focus on understanding the uncertainty in a class-dependent manner, as captured by confidence bounds that we directly embed into the learning process. By incorporating class-dependent estimates, our method can effectively adapt to the varying degrees of imbalance across different classes, resulting in more robust and reliable classification outcomes. We empirically show how our framework provides a promising direction for handling imbalanced data in classification tasks, offering practitioners a valuable tool for building more accurate and trustworthy models.