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English(EN) PaSta: Noisy Node Classification with Partial Label Learning

PaSta框架利用部分标签学习改进噪声节点分类

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种用于噪声节点分类的新型框架PaSta，该框架利用部分标签学习。该方法通过训练多个标注器生成高质量的部分标签来解决现有方法的局限性，然后这些标签被用来指导分类模型。PaSta通过自训练策略进一步增强鲁棒性，迭代地改进标签并优化标注器。实验表明，在各种噪声设置下，PaSta在分类性能方面平均提高了1.1%。 AI

影响 这项研究为在数据不完美的情况下改进现实世界场景中基于图的机器学习模型的准确性提供了一种新颖的方法。

排序理由 该项目是一篇学术论文，详细介绍了一种用于噪声节点分类的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

PaSta框架利用部分标签学习改进噪声节点分类

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该项目是一篇学术论文，详细介绍了一种用于噪声节点分类的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yujing Liu, Yixin Liu, Yu Zheng, Yue Tan, Alan Wee-Chung Liew, Shirui Pan ·

    PaSta：基于部分标签学习的噪声节点分类

    arXiv:2608.25365v1 Announce Type: new Abstract: Noisy node classification problem is a fundamental yet challenging task for real-world graph-related web services, where node labels are often corrupted or unreliable due to weak supervision or automatic annotation. However, existin…

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