图表正面未标记的学习通过引导标签清晰度
Chunquan Liang1, Luyue Wang2, Xinyuan Feng2
1College of Information Engineering, Northwest A&F University, Shaanxi, China; Shaanxi Engineering Research Center for Intelligent Perception and Analysis of Agricultural Information, Shaanxi, China.
概括
引导标签歧义 (BLD) 通过将未标记的节点视为模两可的方法来增强图形的正无标记学习. 这种方法在二进制分类任务中优于现有的方法,甚至是完全标记的模型.
科学领域:
- 机器学习 机器学习
- 图形分析分析 图形分析
- 数据科学数据科学数据科学
背景情况:
- 图形上的正无标记 (PU) 学习对于使用有限的标记数据进行二进制分类至关重要.
- 目前用于PU学习的图形神经网络方法通常使用弱客观函数,限制性能.
- 现有的方法很难与完全监督的方法的性能相匹配.
研究的目的:
- 引入一种新的方法,即引导标签清晰化 (Bootstrap Label Disambiguation,简称BLD),以改善图形PU学习.
- 解决PU学习中现有的目标函数的局限性.
- 开发一种方法,可以超越现有的PU学习技术和完全标记的模型.
主要方法:
- 将未标记的节点视为含糊地标记的 (正和负).
- 实施一个引导标签歧义化 (BLD) 战略,以逐步解决标签歧义问题.
- 使用节点表示学习模块与引导和基于中央区域的明确化策略.
主要成果:
- BLD显著优于现有的图形PU学习方法.
- 在许多情况下,BLD超过了完全标记的分类模型的性能.
- 该方法有效地解决了标签的模两可,并将其转化为有价值的监督.
结论:
- BLD提供了一种强大的新方法来绘制积极的未标记的学习图表.
- 该方法在各种现实数据集中展示了卓越的性能.
- BLD有效地处理标签模两可,导致高度准确的二进制分类.
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