Multivariable data imputation for the analysis of incomplete credit data
作者：
Highlights：
• A novel iterative imputation method for incomplete credit data is proposed.
• The method combines an iterative mechanism and a Bayesian network classifier.
• Our method is less dependent on the hypothesis for probability distribution than other methods.
• The proposed method suits for both single variable and multivariable missing data.
• The proposed method is more accuracy and more applicable than other baseline methods.
摘要
•A novel iterative imputation method for incomplete credit data is proposed.•The method combines an iterative mechanism and a Bayesian network classifier.•Our method is less dependent on the hypothesis for probability distribution than other methods.•The proposed method suits for both single variable and multivariable missing data.•The proposed method is more accuracy and more applicable than other baseline methods.
论文关键词：Bayesian network,Credit scoring,Data missing,Data mining
论文评审过程：Received 15 November 2018, Revised 5 April 2019, Accepted 4 September 2019, Available online 5 September 2019, Version of Record 18 September 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.112926
