积极学习的退学机制基于一个属性启发式的启发式.
Sriram Ravichandran1, Nandan Sudarsanam2,3, Balaraman Ravindran2,3
1Department of Management Studies, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India.
Frontiers in artificial intelligence
|September 18, 2025
概括
这项研究引入了一种新的退学机制,以减少积极学习 (AL) 数据标签中的人类偏见. 该方法通过减轻属性选择偏差,显著提高AL性能,提高模型准确性用更少的样本.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 积极学习 (AL) 通过选择信息样本,训练模型具有最小的标记数据.
- 在AL中的人类注释者可以通过过度依赖特定属性来引入系统偏见.
- 这种偏差可以降低AL系统的效率和准确性.
研究的目的:
- 根据单个属性开发一种数学基础的方法来量化基于单个属性的错误标签概率.
- 引入一种新的脱落机制,以减轻在人类注释期间的属性选择偏差.
- 评估该机制在提高积极学习绩效方面的有效性.
主要方法:
- 开发了一种方法来计算归因于单属性依赖的错误标签的概率.
- 实施了一个新的弃机制,集成到注释过程中,以指导属性选择.
- 在各种任务上评估了各种主动学习算法和启发式策略中的学机制.
主要成果:
- 拟议的退学机制显著改善了积极学习的表现.
- 在实验中观察到至少70%的有效性提升.
- 该机制有效地减少了人类注释者偏差对数据标签的影响.
结论:
- 新型学机制是减少积极学习偏见的可行策略.
- 这种方法提高了AL系统的可靠性和准确性.
- 这些发现为开发更强大的智能系统提供了宝贵的见解.
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