A proposal for PU classification under Non-SCAR using clustering and logistic model

arXiv:2604.17130 · stat.ME, cs.LG, stat.ML · Submitted 2026-04-18 · Read on arXiv

stat.ME, cs.LG, stat.ML

Submitted: 2026-04-18

Updated: 2026-04-18

Comments: 12 pages, 2 figures, MDAI 25

Journal ref: USB Proceedings of the 22nd International Conference on Modeling Decisions for Artificial Intelligence: MDAI 2025, Valencia, Spain 15 - 18 September, 2025 ISBN 978-91-531-0240-3

Code: https://github.com/kapacc/mdai25-clusters-scripts

License: http://creativecommons.org/publicdomain/zero/1.0/

The gist: The present study aims to investigate a cluster cleaning algorithm that is both computationally simple and capable of solving the PU classification when the SCAR condition is unsatisfied.

Terminology

Abstract

The present study aims to investigate a cluster cleaning algorithm that is both computationally simple and capable of solving the PU classification when the SCAR condition is unsatisfied. A secondary objective of this study is to determine the robustness of the LassoJoint method to perturbations of the SCAR condition. In the first step of our algorithm, we obtain cleaning labels from 2-means clustering. Subsequently, we perform logistic regression on the cleaned data, assigning positive labels from the cleaning algorithm with additional true positive observations. The remaining observations are assigned the negative label. The proposed algorithm is evaluated by comparing 11 real data sets from machine learning repositories and a synthetic set. The findings obtained from this study demonstrate the efficacy of the clustering algorithm in scenarios where the SCAR condition is violated and further underscore the moderate robustness of the LassoJoint algorithm in this context.

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