2022/11/01 by Prabhant Singh, Joaquin Vanschoren, Singh, Prabhant +1 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #Machine Learning (cs.LG) #Machine Learning and Data Classification #Statistical and Computational Modeling
paper · doi:10.48550/arxiv.2211.00376
openalex publication_date 2022/11/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Automated Machine Learning has grown very successful in automating the time-consuming, iterative tasks of machine learning model development. However, current methods struggle when the data is imbalanced. Since many real-world datasets are naturally imbalanced, and improper handling of this issue can lead to quite useless models, this issue should be handled carefully. This paper first introduces a new benchmark to study how different AutoML methods are affected by label imbalance. Second, we propose strategies to better deal with imbalance and integrate them into an existing AutoML framework. Finally, we present a systematic study which evaluates the impact of these strategies and find that their inclusion in AutoML systems significantly increases their robustness against label imbalance.