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papersTODAY 04:00 UTC

arXiv paper proposes clustering-assisted logistic model for PU classification beyond SCAR

A new arXiv preprint examines positive-unlabeled (PU) classification when the common SCAR assumption does not hold. The authors study logistic regression approaches, including a cluster-based method and Lasso-regularized variants, and add oversampling to improve performance. The work appears as a cross-listing in cs.AI and cs.LG.

Logistic regression Clustering Lasso regularization Oversampling Positive-unlabeled learning SCAR assumption

COVERAGE · 2 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.AIPU classification under Non-SCAR: clustering-assisted logistic model with oversampling enhancement ↗TODAY 04:00 UTC

arXiv cs.LGPU classification under Non-SCAR: clustering-assisted logistic model with oversampling enhancement ↗TODAY 04:00 UTC