Adaptive pairing of classifier and imputation methods based on the characteristics of missing values in data sets

作者：

Highlights：

• Selection of the optimal combination of imputation method and classifier is very costly.

• A novel method of automatic, adaptive selection of the optimal combination, AMCI, is proposed.

• Successfully demonstrate the superiority of the proposed method with multiple data sets.

• The results also suggest that AMCI is scalable: good for bid data analytics and IoT applications.

摘要

•Selection of the optimal combination of imputation method and classifier is very costly.•A novel method of automatic, adaptive selection of the optimal combination, AMCI, is proposed.•Successfully demonstrate the superiority of the proposed method with multiple data sets.•The results also suggest that AMCI is scalable: good for bid data analytics and IoT applications.

论文关键词：Classification algorithms,Imputation methods,Case-based reasoning,Experiments

论文评审过程：Received 21 October 2014, Revised 3 November 2015, Accepted 5 November 2015, Available online 10 November 2015, Version of Record 1 December 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.11.004