一种基于集群辅助差异演变的混合过量抽样方法,用于不平衡的数据集
Muhammed Abdulhamid Karabiyik1, Bahaeddin Turkoglu2, Tunc Asuroglu3,4
1Department of Computer Engineering, Nigde Omer Halisdemir University, Nigde, Turkey.
PeerJ. Computer science
|September 24, 2025
概括
集群DEBO是一种新的混合过量采样方法,通过使用K-Means集群和差异演化 (DE) 来生成合成数据,有效地解决了类不平衡. 这种方法可以提高对不平衡数据集的分类器性能.
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:
- 数据集中的类不平衡是一个重大挑战,导致有偏见的机器学习模型错误分类少数类实例.
- 像SMOTE这样的现有过量采样技术经常与诸如类重叠,决策边界表现不佳和噪音积累等问题作斗争.
研究的目的:
- 引入ClusterDEBO,一种新的混合过量采样方法,集成K-Means集群和差异演化 (DE).
- 以结构化和自适应的方式生成合成样本,改善对不平衡数据集的处理.
主要方法:
- 该方法将少数类数据划分为集群,使用轮得分来确定集群的最佳数量.
- 基于差异进化的突变和交叉操作在每个集群中产生多样化的合成样本,保持数据分布.
- 选择性采样和降噪机制根据其分类性能影响过合成样品.
主要成果:
- 在使用kNN,DT和SVM分类器的44个基准数据集上评估了ClusterDEBO.
- 提出的方法始终优于现有的过量采样技术,提高了类的分离性和分类器的稳定性.
- 使用弗里德曼测试的统计验证证实了观察到的改善的意义.
结论:
- 集群DEBO提供了一种强大的策略,通过利用集群辅助的差异演变来处理不平衡的数据集.
- 该方法在提高分类器的准确性和稳定性方面表现出优异的性能,与传统的过量采样技术相比.
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