基于抗噪加权模糊粒度量的样本和特征双选择OA

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.