吉林大学学报（信息科学版）2024，Vol.42Issue(2)：312-317,6.
空间自回归模型下不完整大数据缺失值插补算法
Interpolation Algorithm for Missing Values of Incomplete Big Data in Spatial Autoregressive Model
摘要
Abstract
Incomplete big data,due to its irregular structure,has a large amount of computation and low interpolation accuracy when interpolation misses values.Therefore,a missing value interpolation algorithm for incomplete big data based on spatial autoregressive model is proposed.Using a migration learning algorithm to filter out redundant data from the original data under dynamic weights,to distinguish abnormal data from normal data,and to extract incomplete data.Using least square regression to repair the incomplete data.The missing value interpolation is divided into three types,namely,first order spatial autoregressive model interpolation,spatial autoregressive model interpolation,and multiple interpolation.The repaired data is interpolated to the appropriate location according to the actual situation,implementing incomplete big data missing value interpolation.Experimental results show that the proposed method has good interpolation ability for missing values.关键词
迁移学习/不完整大数据/缺失值插补/空间回归模型/数据修正Key words
transfer learning/incomplete big data/imputation of missing values/spatial regression model/data correction分类
信息技术与安全科学引用本文复制引用
刘晓燕,翟建国..空间自回归模型下不完整大数据缺失值插补算法[J].吉林大学学报（信息科学版）,2024,42(2):312-317,6.基金项目
云南省自然科学基金资助项目(202224143456) （202224143456）
