混合集群策略,在多类分类分类中有效地进行过量采样和不足采样
Amirreza Salehi1, Majid Khedmati2
1Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran.
Scientific reports
|January 27, 2025
概括
本研究介绍了一种基于混合集群的过量采样和不足采样 (HCBOU) 技术,以有效处理多类不平衡数据集. 新的算法在各种场景中显著优于现有方法,在不同的不平衡水平上表现出强的性能.
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:
- 现实世界的数据集经常表现出多类不平衡,而罕见的类只有很少的样本.
- 这种不平衡给预测建模和分析带来了重大挑战.
- 现有的方法很难有效地管理不同级别的阶级差异.
研究的目的:
- 引入一种新的基于混合集群的超采样和低采样 (HCBOU) 技术.
- 为应对多类不平衡数据集带来的挑战.
- 为了提高罕见事件数据集的分类性能.
主要方法:
- 在HCBOU算法集群数据,分离多数和少数类.
- 它采用了少数阶级的过量抽样和多数阶级的不足抽样.
- 分类是使用一个对一个和一个对所有分解方案进行的.
主要成果:
- 拟议的HCBOU算法与最先进的方法相比,显示出更高的性能.
- 在30个数据集上的实验证实了算法在各种场景中的有效性.
- 在不同级别的阶级不平衡中,HCBOU表现强.
结论:
- 在处理多类不平衡数据集时,HCBOU算法非常有效.
- 它对不平衡数据的现有技术提供了显著的改进.
- 该方法为具有罕见事件的真实应用提供了强大的解决方案.
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