山西大学学报（自然科学版）2026，Vol.49Issue(1)：29-41,13.DOI:10.13451/j.sxu.ns.2025110
基于抗噪加权模糊粒度量的样本和特征双选择
Bi-selection of Instances and Features Based on Denoising Weighted Fuzzy Granular Measure
摘要
Abstract
Existing feature selection methods based on relative fuzzy rough sets have attempted to characterize instance outlier distri-bution in similarity calculation to enhance robustness,but still fail to effectively suppress potential noise interference and cannot fur-ther compress data scale.To overcome this issue,this paper proposed a bi-selection method using denoise-weighted fuzzy granules(BS-RFRS).A relative distance measure with a denoise discretization factor for adaptive adjustment based on local instance density was designed.Denoise-weighted fuzzy granules were then constructed for model granulation within the bi-selection framework.Based on this granular structure,the paper proposed the BS-RFRS algorithm to maximize data reduction while improving classifica-tion performance.Experiments on 12 benchmark datasets demonstrated that BS-RFRS significantly outperforms five other bi-selec-tion algorithms in classification accuracy and effectiveness.It achieves particularly notable accuracy gains on medical diagnosis and industrial control datasets,and shows improved effectiveness over traditional models.Under label noise,the classification accuracy of BS-RFRS is on average improved by 19.9%and 42.7%compared with the BSNID model and the(bi-selection method based on fuzzy rough sets)(BSFRS)model,respectively.关键词
模糊粗糙集/样本分布密度/抗噪权重/相对距离度量/粒计算Key words
fuzzy rough sets/instance distribution density/denoising weight/relative distance metric/granular computing分类
信息技术与安全科学引用本文复制引用
李嘉豪,折延宏,贺晓丽,钱婷,郑文利..基于抗噪加权模糊粒度量的样本和特征双选择[J].山西大学学报（自然科学版）,2026,49(1):29-41,13.基金项目
国家自然科学基金(12471442) （12471442）
陕西省自然科学基金(2023-JC-YB-027 （）
2025JC-YBMS-034) （）
陕西省教育厅科学研究计划青年创新团队项目(23JP132) （23JP132）
