Semi-Supervised Self-training Approaches for Imbalanced Splice Site Datasets

Abstract

Machine Learning algorithms produce accurate classifiers when trained on large, balanced datasets. However, it is generally expensive to acquire labeled data, while unlabeled data is available in much larger amounts. A cost-effective alternative is to use Semi-Supervised Learning, which uses unlabeled data to improve supervised classifiers. Furthermore, for… (More)

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