PU classification under Non-SCAR: clustering-assisted logistic model with oversampling enhancement

arXiv:2609.14675 · stat.ML, cs.AI, cs.LG · Submitted 2026-09-13 · Read on arXiv

stat.ML, cs.AI, cs.LG

Submitted: 2026-09-13

Updated: 2026-09-13

Comments: 11 pages, 1 figure, 3 tables. Supplementary materials and full reproducible R code available at: https://github.com/kapacc/mdai26

Journal ref: USB Proceedings of the 23nd International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2026, Vic, Catalonia, Spain 7- 9 September 2026, ISBN: 978-91-531-0241-0

Code: https://github.com/kapacc/mdai26

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Clustering — 23× in this paper · explained in 2 episodes
• AUC — 19× in this paper · explained in 2 episodes
• Regularization — 12× in this paper · explained in 3 episodes
• Accuracy — 11× in this paper · explained in 1 episode
• Robustness — 6× in this paper · explained in 5 episodes
• Selected Completely At Random — 3× in this paper
• Benchmark — 2× in this paper · explained in 2 episodes
• Selected At Random — 2× in this paper
• Benchmarking — 1× in this paper · explained in 4 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes

Sources

• Learning from positive and unlabeled data: a survey

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