计算机应用与软件2024，Vol.41Issue(5)：286-297,12.DOI:10.3969/j.issn.1000-386x.2024.05.042
基于稀疏正则化的加权叠加集成多标签分类
WEIGHTED SUPERPOSITION ENSEMBLE MULTIPLE LABEL CLASSIFICATION BASED ON SPARSE REGULARIZATION
摘要
Abstract
In order to fully exploit the correlation of paired labels and the relationship between classifier weight and classifier selection,a weighted superposition ensemble multiple label classification method based on sparse regularization is proposed.A sparse regularized weighted superposition ensemble model was proposed to facilitate the selection of multiple label classifiers and the construction of ensemble members.The classifier weight and label correlation were used to improve the classification performance.An optimization algorithm based on accelerated proximal gradient and block coordinate descent technique was proposed to obtain the optimal solution effectively.Experimental results on several data sets show that the proposed method can effectively achieve high precision multiple label classification.关键词
多标签分类/相关性/稀疏正则化/权值Key words
Multiple label classification/Correlation/Sparse regularization/Weight分类
信息技术与安全科学引用本文复制引用
肖建芳,刘缅芳..基于稀疏正则化的加权叠加集成多标签分类[J].计算机应用与软件,2024,41(5):286-297,12.基金项目
广东省高等学校科研项目(2018GkQNCX150) （2018GkQNCX150）
2020年度广东省普通高校特色创新和青年创新人才项目立项(2020KQNCX209). （2020KQNCX209）
