Training Over a Distribution of Hyperparameters for Enhanced Performance and Adaptability on Imbalanced Classification
Keywords: class imbalance, hyperparameter tuning, ROC curves, machine learning
Abstract: Although binary classification is a well-studied problem, training reliable classifiers under severe class imbalance remains a challenge. Recent techniques mitigate the ill effects of imbalance on training by modifying the loss functions or optimization methods. We observe that different hyperparameter values on these loss functions perform better at different recall values. We propose to exploit this fact by training one model over a distribution of hyperparameter values--instead of a single value--via Loss Conditional Training (LCT). Experiments show that training over a distribution of hyperparameters not only approximates the performance of several models, but actually improves the overall performance of models on both CIFAR and real medical imaging applications such as melanoma and diabetic retinopathy detection. Furthermore, training models with LCT is more efficient because some hyperparameter tuning can be conducted after training to meet individual needs without needing to retrain from scratch. Code will be made available upon acceptance of this paper.
Primary Area: other topics in machine learning (i.e., none of the above)
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Submission Number: 1910
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