AutoML-Med uses Latin Hypercube Sampling and Partial Rank Correlation Coefficient to automatically find preprocessing and model configurations that improve balanced accuracy and sensitivity on imbalanced medical tabular datasets.
Automated Imbalanced Learning
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abstract
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.
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AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data
AutoML-Med uses Latin Hypercube Sampling and Partial Rank Correlation Coefficient to automatically find preprocessing and model configurations that improve balanced accuracy and sensitivity on imbalanced medical tabular datasets.