Deep Learning Meets Oversampling: A Learning Framework to Handle Imbalanced Classification

2025/02/08 by Sukumar Kishanthan, Kishanthan, Sukumar, Asela Hevapathige +1 · 2 citations
Health Professions · Computer Science · #Medical Coding and Health Information #Imbalanced Data Classification Techniques

paper · pdf · doi:10.48550/arxiv.2502.06878

Abstract

Despite extensive research spanning several decades, class imbalance is still considered a profound difficulty for both machine learning and deep learning models. While data oversampling is the foremost technique to address this issue, traditional sampling techniques are often decoupled from the training phase of the predictive model, resulting in suboptimal representations. To address this, we propose a novel learning framework that can generate synthetic data instances in a data-driven manner. The proposed framework formulates the oversampling process as a composition of discrete decision criteria, thereby enhancing the representation power of the model's learning process. Extensive experiments on the imbalanced classification task demonstrate the superiority of our framework over state-of-the-art algorithms.

Cited by

• Orthogonal Activation with Implicit Group-Aware Bias Learning for Class Imbalance
• AxelSMOTE: An Agent-Based Oversampling Algorithm for Imbalanced Classification

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