中南民族大学学报（自然科学版）2024，Vol.43Issue(4)：485-493,9.DOI:10.20056/j.cnki.ZNMDZK.20240408
基于DSGAN-OD模型的文物感知数据缺失值插补方法研究
Missing value imputation method for cultural heritage sensing data based on the DSGAN-OD model
摘要
Abstract
High-quality cultural heritage perception data is of great significance to cultural heritage conservation.However,due to the harsh natural environment conditions of cultural heritages,there are inevitably missing values in the data from sensing devices.And the missing data of the same category in cultural heritages have the characteristics of small sample.The existing missing values processing methods do not take into account the noise interference in cultural heritages data and the spatio-temporal correlation between small sample data,resulting in low accuracy of missing value interpolation.A missing value imputation model based on Semi-Supervised Generative Adversarial Networks(DSGAN-OD)is proposed.In this model,the multi-dimensional data are firstly de-noised and de-dimensional by DAE.Due to the unsupervised attribute of Generation Adversarial Networks,the classification label information in the cultural heritages data cannot be fully utilized.The low-dimensional expression vectors obtained by DAE were used as learning samples of semi-supervised generative adversarial networks(SemiGAN)to obtain features of missing datasets.Meanwhile,Order Decision(OD)method is used to determine the filling order of missing values according to the spatio-temporal correlation between data.Finally,the missing values are interpolated with the complete data generated by SemiGAN in this order.Experimental results on UCI standard dataset and cultural heritages temperature and humidity data show that compared with existing GAIN method,Random Forest method and MICE method,the accuracy of DSGAN-OD missing value interpolation model is improved by 21%,48.2%and 45.1%,respectively.关键词
缺失值插补/文物安防/DSGAN-OD模型/填充顺序决策Key words
missing value imputation/cultural heritages security/DSGAN-OD model/imputation order decision分类
信息技术与安全科学引用本文复制引用
袁小佩,朱容波,王俊,刘浩..基于DSGAN-OD模型的文物感知数据缺失值插补方法研究[J].中南民族大学学报（自然科学版）,2024,43(4):485-493,9.基金项目
国家重点研发计划资助项目(2020YFC1522600) （2020YFC1522600）
