Automated Class Imbalance Learning via Few-shot Bayesian Optimization with Meta-learned Deep Kernel Surrogates

The class imbalance problem is a critical challenge in real-world applications, such as fault diagnosis, intrusion detection, and fraud detection, where the data exhibit highly skewed class distributions. Traditional methods to address class imbalance, such as resampling approaches, require careful model selection and hyperparameter tuning, which are complex and time-consuming. Automated Class Imbalance Learning (AutoCIL) has recently emerged as a promising paradigm, leveraging Combined Algorithm Selection and Hyperparameter Optimization (CASH) to automate this process. However, existing methods often suffer from inefficiencies and ineffectiveness, especially under resource constraints. In this paper, we propose a novel method called AutoCILFBO – Automated Class Imbalance Learning via Few-shot Bayesian Optimization with Meta-learned Deep Kernel Surrogates. Our approach introduces few-shot Bayesian optimization with deep kernel Gaussian processes tailored for class imbalance domains. Specifically, we meta-learn a shared probabilistic deep kernel surrogate model from a collection of pre-evaluated class imbalance optimization tasks, enabling rapid adaptation to target tasks. Experimental results demonstrate that our method outperforms existing approaches across 16 tasks with statistically significant improvements in terms of efficiency and effectiveness.