aiqbench analysis
This research advances unsupervised learning by refining Positive-Unlabeled (PU) classification methods through SMOTE-based oversampling and clustering to handle violations of the SCAR assumption. The study demonstrates that these enhancements significantly improve performance on benchmark datasets where standard assumptions fail, offering a robust approach for imbalanced data scenarios common in real-world AI applications.
PU classification under Non-SCAR: clustering-assisted logistic model with oversampling enhancement
arXiv:2609.14675v1 Announce Type: cross Abstract: 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 remaining observations are treated as negative. The experimental evaluation is conducted on 13 real benchmark datasets and one synthetic dataset. For comparison, we include the naive approach and the Spy-EM method. The results demonstrate that incorporating SMOTE improves classification performance when the SCAR condition is violated and indicate moderate robustness of the LassoJoint method in this setting.
Abstract: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 remaining observations are treated as negative. The experimental evaluation is conducted on 13 real benchmark datasets and one synthetic dataset. For comparison, we include the naive approach and the Spy-EM method. The results demonstrate that incorporating SMOTE improves classification performance when the SCAR condition is violated and indicate moderate robustness of the LassoJoint method in this setting.
11 pages, 1 figure, 3 tables. Supplementary materials and full reproducible R code available at: this https URL
Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
I.2.6; I.5.1; G.3
arXiv:2609.14675 [stat.ML]
(or arXiv:2609.14675v1 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2609.14675 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 276 words
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