提出新方法解决非SCAR条件下正负样本不均衡的分类问题。
A proposal for PU classification under Non-SCAR using clustering and logistic model
- 用2均值聚类生成清洗标签，再做逻辑回归分类。
- 在11个真实数据集上验证，有效应对SCAR条件不满足的情况。
- 适合处理标注不完整、样本分布不均衡的实际场景。
本研究旨在探索一种计算简单且能在非SCAR条件下有效解决PU分类问题的聚类清洗算法。首先通过2均值聚类获得清洗标签，随后在清洗后的数据上进行逻辑回归，将聚类结果中的正类样本与额外的真实正例合并标记为正，其余样本标记为负。该方法在11个来自机器学习库的真实数据集及一个合成数据集上进行了评估。结果显示，该聚类算法在违反SCAR假设的场景中仍具有效性，同时表明LassoJoint方法在该背景下具有一定的鲁棒性。
原文摘要 · Abstract (English)
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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