山东大学学报 (工学版) ›› 2026, Vol. 56 ›› Issue (1): 35-48.doi: 10.6040/j.issn.1672-3961.0.2024.200
• 机器学习与数据挖掘 • 上一篇
李璐1,王鑫2
LI Lu1, WANG Xin2
摘要: 提出一种鲁棒性模糊粗糙集模型和属性约简算法。考虑数据样本的局部密度并进行量化,利用量化结果评估样本在数据集整体中的噪声程度;通过噪声程度衡量样本的权重,定义一种样本集之间的距离度量,并将样本之间的模糊相似性替换为样本集之间的模糊相似性,提升模糊相似关系的鲁棒性,建立一种鲁棒性模糊粗糙集模型;基于所提出的鲁棒性模糊粗糙集定义属性与类之间的依赖度,以评估属性子集的显著性,并设计一种鲁棒性模糊粗糙集的属性约简算法。试验结果表明,所设计的属性约简算法比现有的算法具有更强的鲁棒性和优越性。
中图分类号:
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