PU classification under Non-SCAR: clustering-assisted logistic model with oversampling enhancement
This study addresses the PU classification problem under violations of the SCAR assumption. We investigate logistic regression-based approaches, namely the cluster method and its extensions with strict and non-strict Lasso regularization. The primary contribution of this work is the integration of the SMOTE technique to alleviate class imbalance and systematically assess its impact on the performance of the considered algorithms. SMOTE is first applied to rebalance the training dataset. Next, cleaning labels are derived via 2-means clustering. Logistic regression is then trained on the cleaned data, where identified positive instances are augmented with additional true positives and the r...
| Authors | Konrad Furmańczyk, Kacper Paczutkowski |
|---|---|
| Primary category | stat.ML |
| Published | 13 September 2026 |
| Last updated at source | 13 September 2026 |
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