[1]张震,春美洁,田鸿朋,等.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.[doi:10.13705/j.issn.1671-6833.2026.04.006]
　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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自适应子空间插补的不完整数据证据集成分类()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
47
期数:
2026年4期
页码:
50-57
栏目:
出版日期:
2026-07-10

文章信息/Info

Title:
Ensemble Classification of Incomplete Data Evidence on Adaptive Subspace Imputation
文章编号:
1671-6833(2026)04-0050-08
作者:
张震1,2, 春美洁1, 田鸿朋2, 李友好3, 黄伟涛3, 张俊杰3
1. 郑州大学 河南先进技术研究院,河南 郑州 450001;2. 郑州大学 电气与信息工程学院,河南 郑州 450001;3. 河南汇融油气技术有限公司,河南 郑州 450001
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, China; 2. School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; 3. Henan Huirong Oil and Gas Technology Co. , Ltd. , Zhengzhou 450001, China
关键词:
不完整数据;  分类;  全局重要性;  局部重要性;  证据理论
Keywords:
incomplete data;  classify;  global importance;  local importance;  evidential reasoning
分类号:
TP181
DOI:
10.13705/j.issn.1671-6833.2026.04.006
文献标志码:
A
摘要:
针对基于插补的分类方法在处理缺失数据时的估计值偏差会影响分类性能的问题,提出一种基于自适应子空间插补的不完整数据证据集成分类方法,利用自适应子空间插补和双重证据集成来提升模型对不完整数据集的分类能力。 首先,使用谱聚类将特征空间动态划分为多个子空间,在每个子空间内独立进行基于近邻的缺失值插补;其次,设计了一种双重重要性评估机制,计算插补前后训练集数据分布的差异性来评估全局重要性,并通过评估分类模型对测试集样本在训练集中近邻的分类能力来评估其分类结果的局部重要性;最后,在证据理论的基础上融合局部重要性和全局重要性,利用不同子空间信息的互补性提升分类性能。 在标准数据集上的对比实验表明:所提方法在 ARI 和 AP 指标中相比次优方法的提升幅度分别高达 6. 23 百分点和 0. 82 百分点,验证了所提方法的有效性和先进性。
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.

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更新日期/Last Update: 2026-10-21