山西大学学报（自然科学版）2025，Vol.48Issue(6)：1092-1102,11.DOI:10.13451/j.sxu.ns.2025017
基于密度峰值的粒球邻域粗糙集
Granular-ball Neighborhood Rough Set Based on Density Peak
摘要
Abstract
Attribute reduction is one of the commonly used techniques in data analysis and modeling.The granular-ball neighborhood rough set,which can adaptively set the neighborhood radius,enhances the accuracy and robustness of attribute reduction.However,current granular-ball generation methods face problems of uncertain numbers and unstable distributions.To address this issue,this paper proposed a granular-ball generation method based on density peaks.By using density peak points and the nearest centroid points as centers,this method ensures that the centers are composed of sample points,thereby enhancing the interpretability of granu-lar-balls.Based on this new granular-ball generation method,a granular-ball neighborhood rough set model based on density peaks was derived.This model overcomes the limitation of using the positive region for attribute reduction in granular-ball neighborhood rough sets.And accordingly a backward attribute reduction algorithm was designed.The above algorithm was tested on multiple da-tasets.Experimental results show that,compared to existing methods,the new model achieves stable performance during the granu-lar-ball generation process,and the reduced attributes significantly enhance classification performance.关键词
粒计算/多粒度粒球计算/邻域粗糙集/密度聚类/属性约简Key words
granular computing/multi-granularity granular-ball computing/neighborhood rough set/density clustering/attribute re-duction分类
物理学引用本文复制引用
朱学勤,邵亚斌,华有霖..基于密度峰值的粒球邻域粗糙集[J].山西大学学报（自然科学版）,2025,48(6):1092-1102,11.基金项目
国家自然科学基金(12061067 （）
62176033) （）
重庆市自然科学基金(CSTB2023NSCQ-MSX0707) （CSTB2023NSCQ-MSX0707）
