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节点级感知边缘采样减轻了在生物医学随机步行基于图形表示学习中的膨胀分类性能.

Luca Cappelletti1, Lauren Rekerle2, Tommaso Fontana1

  • 1AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Milano 20133, Italy.

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概括
此摘要是机器生成的。

在图形表示学习中,标准的负边缘采样会产生不平衡的节点度,影响生物医学机器学习. 一种新的程度感知采样方法提高了模型评估的准确性.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 机器学习 机器学习
  • 图形理论就是图形理论.

背景情况:

  • 图形表示学习为节点和图形元素生成嵌入,这对于预测新关系 (边缘) 等任务很有用.
  • 生物医学知识图通常具有已知的正关系,但缺乏明确的负关系,迫使模型假设大多数未标记的边缘是负的.
  • 当前的方法均地采样负边,导致正和负示例之间的节点度分布不平衡.

研究的目的:

  • 调查统一负边缘采样对生物医学应用中的图形表示学习性能的影响.
  • 开发和介绍一种新的,程度意识的节点采样方法,以减轻负面示例选择中的偏差.

主要方法:

  • 使用了一个代表性的异质生物医学知识图.
  • 采用基于随机走路的图形机器学习技术.
  • 实施并比较了一种新的度意识节点抽样策略与统一抽样.

主要成果:

  • 统一的负边取样导致节点度分布不平衡,显著影响分类性能.
  • 这种不平衡可以在验证过程中人工膨胀模型性能估计.
  • 提出的程度意识抽样方法有效地减轻了这种偏差,从而导致更准确的模型评估.

结论:

  • 选择负面示例的方法对于准确的图形表示学习至关重要,特别是在生物医学中.
  • 度意识节点采样为训练和评估基于图形的机器学习模型提供了更可靠的方法.
  • 开发的方法是公开的,研究人员可以很容易地实施.