Ensemble Classification of Incomplete Data Evidence on Adaptive Subspace Imputation
[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(4):50-57.[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 Issue 4
Page number:
50-57
Column:
Public date:
2026-07-10
- Title:
- Ensemble Classification of Incomplete Data Evidence on Adaptive Subspace Imputation
- CLC:
- TP181
- Abstract:
- In response to the issue of biased estimates affecting classification performance in imputation-based clas-sificationmethods 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-tionbased on neighbors was performed independently within each subspace. Secondly, a dual importance evalua-tionmechanism 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-gingthe 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.
- References:
-
[1] Wu Han, Wang Shitong. Privileged LSSVM for classification and simultaneous importance identification of missing information on incomplete data[J]. CAAI Transactions on Intelligent Systems, 2023, 18(4): 743–753. [吴晗，王士同. 不完整数据分类与缺失信息重要性识别特权LSSVM[J]. 智能系统学报，2023，18(4)：743–753.]
[2] Radford A, Metz L, Chintala S, et al. Unsupervised representation learning with deep convolutional generative adversarial networks[PP/OL]. V2. arXiv (2016−01−07)[2026−01−19]. https://arxiv.org/abs/1511.06434.
[3] Xu Hongyan, Sun Yunshan, Qin Qilin, et al. Comparative analysis of the performance of interpolation methods for missing data[J]. Software Engineering, 2021, 24(11): 11–14. [徐鸿艳，孙云山，秦琦琳，等. 缺失数据插补方法性能比较分析[J]. 软件工程，2021，24(11)：11–14.]
[4] Shafer G. A mathematical theory of evidence[M]. Princeton: Princeton University Press, 1976.
[5] Tian Hongpeng, Zhang Zuowei, Ding Weiping. Incomplete data transfer calibration classification[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2025, 9(5): 3244–3256.
[6] Tian Hongpeng, Wang Xiaole, Tan Yongguang. Incomplete data evidential classification with inconsistent distribution[J]. Information Sciences, 2024, 676: 120824.
[7] Duan Zhongxing, Bi Hanyuan, Zhang Zuowei. A D-S evidential reasoning based hybrid classification algorithm for incomplete data[J]. Information and Control, 2020, 49(4): 455–463. [段中兴，毕瀚元，张作伟. 基于D-S证据理论的不完整数据混合分类算法[J]. 信息与控制，2020，49(4)：455–463.]
[8] Razavi-Far R, Cheng Boyuan, Saif M, et al. Similarity-learning information-fusion schemes for missing data imputation[J]. Knowledge-Based Systems, 2020, 187: 104805.
[9] Zhan Zhaokang, Hu Xuguang, Zhao Haoran, et al. Study of missing value imputation in wind turbine data based on multivariate spatiotemporal integration network[J]. Acta Automatica Sinica, 2024, 50(6): 1171–1184. [詹兆康，胡旭光，赵浩然，等. 基于多变量时空融合网络的风机数据缺失值插补研究[J]. 自动化学报，2024，50(6)：1171–1184.]
[10] Yoon J, Jordon J, Van Der Schaar M. GAIN: missing data imputation using generative adversarial nets[PP/OL]. V1. arXiv (2018−06−07)[2026−01−19]. https://arxiv.org/abs/1806.02920.
[11] Wen Yizhu, Wang Yiwei, Yi Kai, et al. DifImpute: tabular data imputation with denoising diffusion probabilistic model[C]//Proceedings of the 2024 IEEE International Conference on Multimedia and Expo (ICME). Piscataway: IEEE, 2024: 1–6.
[12] Zhang Zhen, Tian Hongpeng. Hybrid imputation-based optimal evidential classification for missing data[J]. Applied Intelligence, 2025, 55: 69.
[13] Tao Yang, Zhu Xiaojun, Yang Liu. Multi-sensor data fusion based on Pearson coefficient and information entropy[J]. Journal of Chinese Computer Systems, 2023, 44(5): 1075–1080. [陶洋，祝小钧，杨柳. 基于皮尔逊相关系数和信息熵的多传感器数据融合[J]. 小型微型计算机系统，2023，44(5)：1075–1080.]
[14] Yuan Hong, Zhang Hongyan. Research on text feature segmentation based on the efficient spectral clustering algorithm[J]. Changjiang Information & Communications, 2025, 38(5): 171–173. [原虹，张鸿雁. 基于高效谱聚类算法的文本特征分割研究[J]. 长江信息通信，2025，38(5)：171–173.]
[15] Yan Ling, Zhao Hailiang. A privacy budget allocation method based on differential privacy k-means++[J]. Journal of Information Security Research, 2025, 11(8): 710–717. [晏玲，赵海良. 基于差分隐私k-means++的一种隐私预算分配方法[J]. 信息安全研究，2025，11(8)：710–717.]
[16] Kelly M, Longjohn R, Nottingham K. The UCI machine learning repository[DS/OL]. [2026−01−19]. https://archive.ics.uci.edu.
[17] Fu Haitao, Zhang Zhiyong, Wang Zenghui, et al. Improve SHO algorithm to optimize random forest model[J]. Journal of Jilin University (Science Edition), 2025, 63(3): 861–866. [付海涛，张智勇，王增辉，等. 改进SHO算法优化随机森林模型[J]. 吉林大学学报(理学版)，2025，63(3)：861–866.]
[18] Li Song, Liu Xiaonan, Liu Juan. Peak clustering algorithm for uncertain data density based on JS divergence[J]. Journal of Jilin University (Engineering and Technology Edition), 2024, 54(7): 2038–2048. [李松，刘晓楠，刘娟. 基于JS散度的不确定数据密度峰值聚类算法[J]. 吉林大学学报(工学版)，2024，54(7)：2038–2048.]
[19] Tian Hongpeng, Zhang Zhen, Zhang Siyuan, et al. Imbalanced data evidential classification with composite reliability[J]. Journal of Zhengzhou University (Engineering Science), 2023, 44(4): 22–28. [田鸿朋，张震，张思源，等. 复合可靠性分析下的不平衡数据证据分类[J]. 郑州大学学报(工学版)，2023，44(4)：22–28.]
[20] Smets P. Decision making in the TBM: the necessity of the pignistic transformation[J]. International Journal of Approximate Reasoning, 2005, 38(2): 133–147.
[21] Hubert L, Arabie P. Comparing partitions[J]. Journal of Classification, 1985, 2(1): 193–218.
[22] Ren Shaoqing, He Kaiming, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137–1149.
[23] Yue Yong, Tian Kaocong. Summary of data missing and its filling methods[J]. Journal of Preventive Medicine Information, 2005, 21(6): 683–685. [岳勇，田考聪. 数据缺失及其填补方法综述[J]. 预防医学情报杂志，2005，21(6)：683–685.]
[24] Van Buuren S, Groothuis-Oudshoorn K. MICE: multivariate imputation by chained equations in R[J]. Journal of Statistical Software, 2000, 45(3): 1–67.
[25] Ma Zongfang, Ma Xiangshuang, Song Lin, et al. Incomplete data belief classification algorithm based on adaptive KNN imputation[J]. Computer Measurement & Control, 2021, 29(10): 164–169. [马宗方，马祥双，宋琳，等. 异常信息的智能分类算法研究[J]. 计算机测量与控制，2021，29(10)：164–169.][26] Lai Xiaochen, Zhang Zheng, Chen Hui, et al. Tracking-removed neural network with graph information for classification of incomplete data[J]. Applied Intelligence, 2025, 55(4): 256.
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Last Update:
2026-10-21
