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BlindSMOTE:仅基于进化计算的合成少数人过量采样.
Nicolás E Garcí-Pedrajas1, José M Cuevas-Muñoz2, Aida de Haro-García3
1Department of Computer Science, University of Córdoba, 14071 Córdoba, Spain npedrajas@uco.es.
Evolutionary computation
|April 29, 2025
概括
本研究引入了一种进化计算方法,以解决数据挖掘中的阶级不平衡问题,通过生成合成少数群体样本和低于多数阶级的样本来优于现有方法.
科学领域:
- 数据挖掘和机器学习
- 人工智能的人工智能
- 计算智能是一种计算智能.
背景情况:
- 阶级不平衡是现实世界数据挖掘中普遍存在的问题,在现实世界数据挖掘中,少数阶级的代表性不足,这会对分类器的表现产生负面影响.
- 合成少数群体过量采样技术 (SMOTE) 和其变体是常见的解决方案,但依赖于合成样本生成的特定假设.
- 现有的方法经常对合成实例创建施加约束,可能会限制它们的有效性.
研究的目的:
- 提出一种使用进化计算而没有对合成实例生成的限制的新类失衡处理方法.
- 在进化框架内整合多数阶级低抽样.
- 证明拟议方法在现有策略上的优越性.
主要方法:
- 基于纯粹进化计算的阶级失衡解决方案的开发.
- 在没有预定义约束的情况下生成合成少数类样本.
- 在进化过程中纳入多数类低样本的纳入.
- 在多个数据集和现有策略中进行了广泛的比较分析.
主要成果:
- 提出的进化计算方法在解决阶级不平衡方面表现出了显著的优势.
- 该方法在各种分类方法和大量数据集中显示出更好的性能.
- 无约束的合成样本生成和集成的低样本采集证明是有效的.
结论:
- 进化计算提供了一个灵活而强大的框架来解决数据挖掘中的阶级不平衡问题.
- 拟议的方法为现有的基于SMOTE的技术提供了可靠的替代方案.
- 这种方法通过不受约束的合成数据生成和低采样,提高了对不平衡数据集的分类器性能.
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