OMAL:从数据流中采用多标签主动学习方法
Qiao Fang1, Chen Xiang1, Jicong Duan1
1School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China.
Entropy (Basel, Switzerland)
|April 26, 2025
概括
本研究引入了一种新的在线多标签主动学习 (OMAL) 算法,以应对动态数据挑战. 欧马尔算法有效地适应变化的标签相关性和不平衡的数据,在动态环境中优于现有的方法.
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:
- 数字技术的进步产生了复杂,动态的数据流.
- 现实世界的数据往往表现出复杂的类型,如多标签属性.
- 在线学习场景在适应标签相关性和数据不平衡方面存在挑战.
研究的目的:
- 提出一个新的在线多标签主动学习 (OMAL) 算法.
- 为了应对在线场景中的动态标签相关性和不平衡数据分布的挑战.
- 在动态的多标签学习环境中减少标签消费.
主要方法:
- 开发了一个使用不确定性和多样性作为主动查询策略的OMAL算法.
- 使用分类器链 (CC) 进行多标签学习,结合标签共发生排名策略.
- 集成重量极端学习机器 (WELM) 作为处理不平衡数据的基础二进制类分类器.
主要成果:
- 拟议的OMAL算法与静态多标签主动学习算法相比,表现优越.
- 在十个基准多标签数据集上进行评估,转化为数据流.
- 在宏观F1和微型F1指标方面取得了显著的改进.
结论:
- 在动态数据流环境中,OMAL算法是有效的.
- 该方法成功地适应了标签相关性和不平衡数据分布的变异.
- 拟议的方法为在线多标签学习挑战提供了强大的解决方案.
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