计算机应用研究2024，Vol.41Issue(7)：2079-2086,8.DOI:10.19734/j.issn.1001-3695.2023.11.0550
增强学习标签相关性的多标签特征选择方法
Multi-label feature selection method with enhanced learning of label correlations
摘要
Abstract
Aiming at two problems of existing multi-label feature selection methods:first,ignoring the influence of noise infor-mation in the process of learning label correlations;second,neglecting to explore the comprehensive label information of each cluster,the paper proposed a multi-label feature selection method that enhanced label correlation learning.Initially,it clus-tered the samples and treated each cluster center as a representative instance of the comprehensive semantic information of the samples,while computing its corresponding label vectors which reflected the importance of different labels contained in each cluster.Then,through the label-level self-representation of the original samples and the center of each cluster,it both cap-tured the label correlations in the original label space,and explored the label correlations within each cluster.Finally,the self-representation coefficient matrix was sparse to reduce the effect of noise,and the original sample and the representative in-stance of each cluster were mapped from the feature space to the reconstructed label space for feature selection.Experimental results on nine multi-labeled datasets show that the proposed algorithm has better performance compared with other methods.关键词
多标签学习/特征选择/标签相关性/聚类Key words
multi-label learning/feature selection/label correlation/clustering分类
信息技术与安全科学引用本文复制引用
滕少华,卢建磊,滕璐瑶,张巍..增强学习标签相关性的多标签特征选择方法[J].计算机应用研究,2024,41(7):2079-2086,8.基金项目
国家自然科学基金资助项目(6197210) （6197210）
广州市科技计划资助项目(2023A04J1729) （2023A04J1729）
