电子科技2026，Vol.39Issue(7)：24-32,9.DOI:10.16180/j.cnki.issn1007-7820.2026.07.004
基于核密度峰值聚类和细粒度噪声抑制的特征选择方法
Feature Selection Method Based on Kernel Density Peak Clustering and Fine-Grained Noise Suppression
摘要
Abstract
In view of the problems that traditional feature selection methods are vulnerable to noise and the ob-tained feature space is prone to cause changes in data distribution,this study proposes a FNKC(Feature Selection based on Fine-Grained Noise Suppression and Kernel Density Peak Clustering).To overcome the influence of noise on feature selection,the possibility theorem of the PFCM(Possibilistic Fuzzy C-means Clustering Algorithm)is uti-lized in combination with the information particle criterion to extract data correlation,thereby improving the anti-noise performance.Introducing kernel density in high-dimensional space to measure clustering density can accurately cap-ture the spatial structure within clusters,thereby better reflecting the distribution of data.Finally,the superiority and effectiveness of the proposed algorithm are verified by comparing multiple advanced feature selection methods on six public high-dimensional datasets.关键词
特征选择/模糊聚类/核密度/信息粒/先验知识/机器学习/数据挖掘/统计学Key words
feature selection/fuzzy clustering/kernel density/information granularity/prior knowledge/machine learning/data mining/statistics分类
信息技术与安全科学引用本文复制引用
梁润辰,宋燕,窦军..基于核密度峰值聚类和细粒度噪声抑制的特征选择方法[J].电子科技,2026,39(7):24-32,9.基金项目
国家自然科学基金(62073223) （62073223）
上海市自然科学基金(22ZR1443400) National Natural Science Foundation of China(62073223) （22ZR1443400）
Natural Science Foundation of Shanghai(22ZR1443400) （22ZR1443400）
