通过加权损失进行类不平衡的互补标签学习
Meng Wei1, Yong Zhou1, Zhongnian Li1
1School of Computer Science & Technology, China University of Mining and Technology, Xuzhou, China.
概括
本研究介绍了加权补充标签学习 (WCLL),以解决补充标签学习 (CLL) 中的类不平衡,以提高现实数据集的分类准确性.
科学领域:
- 机器学习 机器学习
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 补充标签学习 (CLL) 是一种监督较弱的分类方法.
- 现实世界数据集经常表现出类不平衡,降低了CLL的性能.
- 现有的CLL方法无法解决类不平衡问题.
研究的目的:
- 提出一种新的学习问题设置,在多类分类中使用类不平衡的互补标签.
- 引入加权补充标签学习 (WCLL) 来应对这一挑战.
主要方法:
- 开发了一种新的加权补充标签学习 (WCLL) 方法.
- 为不平衡的补充标签建了一个加权的经验风险最小化损失模型.
- 导出了一个估计误差,限制了理论保证.
主要成果:
- WCLL在基准和现实世界数据集上取得了显著的改进.
- 该方法有效地处理多类不平衡场景.
- 与最先进的方法相比,实现了更高的性能.
结论:
- 在补充标签学习中,WCLL成功地解决了阶级不平衡的挑战.
- 拟议的方法提供了一个强大的解决方案,用于对数据不平衡的低监督分类.
- WCLL既提供了实际的性能提升,也提供了理论验证.
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