郑州大学学报（工学版）2026，Vol.47Issue(4)：50-57,8.DOI:10.13705/j.issn.1671-6833.2026.04.006
自适应子空间插补的不完整数据证据集成分类
Ensemble Classification of Incomplete Data Evidence on Adaptive Subspace Imputation
摘要
Abstract
In response to the issue of biased estimates affecting classification performance in imputation-based clas-sification methods when dealing with missing data,an incomplete data evidence ensemble classification method based on adaptive subspace imputation was proposed.The proposed method utilized adaptive subspace imputation and dual evidence integration to enhance the model's classification ability on incomplete datasets.Firstly,spectral clustering was used to dynamically partition the feature space into multiple subspaces,where missing value imputa-tion based on neighbors was performed independently within each subspace.Secondly,a dual importance evalua-tion mechanism was designed,which calculated the difference in data distribution before and after imputation in the training set to assess global importance,and evaluated the local importance of classification results by assessing the classification capacity of the classification model on the test set samples' neighbors in the training set.Finally,based on evidence theory,local and global importance were fused to enhance classification performance by levera-ging the complementarity of information from different subspaces.Comparative experiments on standard datasets showed that the proposed method achieved improvements of up to 6.23 percentage and 0.82 percentage,respective-ly,in the ARI and AP metrics compared to suboptimal methods,validating the effectiveness and advancement of the proposed method.关键词
不完整数据/分类/全局重要性/局部重要性/证据理论Key words
incomplete data/classify/global importance/local importance/evidential reasoning分类
信息技术与安全科学引用本文复制引用
张震,春美洁,田鸿朋,李友好,黄伟涛,张俊杰..自适应子空间插补的不完整数据证据集成分类[J].郑州大学学报（工学版）,2026,47(4):50-57,8.基金项目
河南省重点研发专项(231111211600) （231111211600）
河南省国际科技合作重点项目(231111520300) （231111520300）
