Instance selection based on boosting for instance-based learners

作者：

Highlights：

• We propose a new instance selection algorithm based on boosting principles.

• The method achieves better storage reduction and testing error than previous approaches.

• The method is scalable using stratified random sampling.

摘要

•We propose a new instance selection algorithm based on boosting principles.•The method achieves better storage reduction and testing error than previous approaches.•The method is scalable using stratified random sampling.

论文关键词：Instance selection,Boosting,Instance-based learning

论文评审过程：Received 4 April 2018, Revised 4 March 2019, Accepted 8 July 2019, Available online 9 July 2019, Version of Record 13 July 2019.

论文官网地址：https://doi.org/10.1016/j.patcog.2019.07.004