基于信心的PU学习与依赖实例的标签噪声
IEEE transactions on neural networks and learning systems
|March 24, 2025
概括
本研究介绍了以实例依赖标签噪声 (PUIDN) 进行积极和未标记的学习,这是一种在机器学习中处理杂的积极标签的新方法. 它有效地利用信心分数减轻噪声影响,提高了分类器的准确性.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 积极和未标记 (PU) 学习只使用PU数据来训练分类器.
- 传统的PU学习假定准确的积极标签,这在实践中往往不是真的.
- 积极集合中的标签噪声是常见的,并且可以是依赖实例的.
研究的目的:
- 为了解决PU学习与实例依赖标签噪声 (PUIDN) 的研究不足的问题.
- 开发一种方法,可以减轻噪音积极标签的不利影响,而不需要假设噪音分布.
- 提高PU学习算法的稳定性和准确性.
主要方法:
- 在积极集中的每个实例中,利用信任得分.
- 建议使用标签和信任信息对分类风险进行公正的估计.
- 整合基于信任相关性的交替代优化策略.
主要成果:
- 拟议的方法有效地处理PU学习中的依赖实例的标签噪声.
- 从PUIDN数据开发并计算出一个不偏见的风险估计器.
- 该框架通过实验验证证明了性能改进.
结论:
- 开发的方法为PU学习提供了强大的解决方案,具有实例依赖的标签噪声.
- 信任度得分对于在杂的场景中将样品和标签连接起来至关重要.
- 该方法提供了理论概括的错误限制和实际有效性.
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