Metric-DST, a diversity-guided self-training method that samples pseudo-labeled points across a metric-learned embedding space, improves robustness to selection bias compared with supervised learning and confidence-based self-training, with modest gains across benchmarks.
Reject inference in credit scoring using a three-way decision and safe semi-supervised support vector machine
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Metric-DST: Mitigating Selection Bias Through Diversity-Guided Semi-Supervised Metric Learning
Metric-DST, a diversity-guided self-training method that samples pseudo-labeled points across a metric-learned embedding space, improves robustness to selection bias compared with supervised learning and confidence-based self-training, with modest gains across benchmarks.