Document Type
Article
Publication Date
6-19-2024
Publication Title
BMC bioinformatics
Department
Geisel School of Medicine
Abstract
Compared to traditional supervised machine learning approaches employing fully labeled samples, positive-unlabeled (PU) learning techniques aim to classify “unlabeled” samples based on a smaller proportion of known positive examples. This more challenging modeling goal reflects many real-world scenarios in which negative examples are not available—posing direct challenges to defining prediction accuracy and robustness. While several studies have evaluated predictions learned from only definitive positive examples, few have investigated whether correct classification of a high proportion of known positives (KP) samples from among unlabeled samples can act as a surrogate to indicate model quality.
DOI
https://doi.org/10.1186/s12859-024-05834-2
Dartmouth Digital Commons Citation
Xu, Shiwei and Ackerman, Margaret E., "Leveraging permutation testing to assess confidence in positive-unlabeled learning applied to high-dimensional biological datasets" (2024). Dartmouth Scholarship. 4376.
https://digitalcommons.dartmouth.edu/facoa/4376
