Ensemble Classification of Incomplete Data Evidence on Adaptive Subspace Imputation

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Ensemble Classification of Incomplete Data Evidence on Adaptive Subspace Imputation

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[1]ZHANG Zhen,CHUN Meijie,TIAN Hongpeng,et al.Ensemble Classification of Incomplete Data Evidence on Adaptive Subspace Imputation[J].Journal of Zhengzhou University (Engineering Science),2026,47(XX):1-8.[doi:10.13705/j.issn.1671-6833.2026.04.006]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 47 Number of periods: 2026 XX Page number: 1-8 Column: Public date: 2026-09-10

Title:: Ensemble Classification of Incomplete Data Evidence on Adaptive Subspace Imputation

Author(s):: ZHANG Zhen¹; 2; CHUN Meijie¹; TIAN Hongpeng²; LI Youhao³; HUANG Weitao³; ZHANG Junjie³; 1. Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou 450001; 2.School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001;3.Henan Huirong Oil and Gas Technology Co., Ltd.,Zhengzhou 450001, China

Keywords:: incomplete data; classify; global importance; local importance; evidential reasoning

CLC:: TP181

DOI:: 10.13705/j.issn.1671-6833.2026.04.006

Abstract:: In response to the issue of biased estimates affecting classification performance in imputation-based classification 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 imputation based on neighbors was performed independently within each subspace. Secondly, a dual importance evaluation mechanism was designed, which calculated the difference in the 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 leveraging 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, respectively, in the ARI and AP metrics compared to suboptimal methods, validating the effectiveness and advancement of the proposed method.

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Last Update: 2026-02-27