Reliability-enhanced data cleaning in biomedical machine learning using inductive conformal prediction.

ABSTRACT: Accurately labeling large datasets is important for biomedical machine learning yet challenging while modern data augmentation methods may generate noise in the training data, which may deteriorate machine learning model performance. Existing approaches addressing noisy training data typically rely on strict modeling assumptions, classification models and well-curated dataset. To address these, we propose a novel reliability-based training-data-cleaning method employing inductive conformal prediction (ICP). This method uses a small set of well-curated training data and leverages ICP-calculated reliability metrics to selectively correct mislabeled data and outliers within vast quantities of noisy training data. The efficacy is validated across three classification tasks with distinct modali

SUBMITTER: Zhan X 

PROVIDER: S-EPMC11870354 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature