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Improving classification on imbalanced genomic data via KDE–based synthetic sampling

2025-05-08

Abstract excerpt

<title>Abstract</title> <p>Class imbalance poses a serious challenge in biomedical machine learning, particularly in genomics, where datasets are characterized by extremely high dimensionality and very limited sample sizes. In such settings, standard classifiers tend to favor the majority class, leading to biased predictions --- an especially problematic issue in clinical diagnostics where rare conditions must no...

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Literature Corpus work: 81749b8d-c621-53e5-987a-3d130bc76457
DOI: 10.21203/rs.3.rs-6513655/v1

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