Adaptive imputation of missing values for incomplete pattern classification

作者：

Highlights：

• Missing values are adaptively imputed in classification according to context.

• SOM and K-NN are used for the imputation with admissible computation burden.

• Ensemble classifier is introduced for credal classification.

• The imprecision of classification can be well captured using belief functions.

• The proposed method has been tested by artificial and real data sets.

摘要

Highlights•Missing values are adaptively imputed in classification according to context.•SOM and K-NN are used for the imputation with admissible computation burden.•Ensemble classifier is introduced for credal classification.•The imprecision of classification can be well captured using belief functions.•The proposed method has been tested by artificial and real data sets.

论文关键词：Belief function,Classification,Missing values,SOM,K-NN

论文评审过程：Received 1 June 2015, Revised 29 September 2015, Accepted 1 October 2015, Available online 20 October 2015, Version of Record 24 December 2015.

论文官网地址：https://doi.org/10.1016/j.patcog.2015.10.001