Three new instance selection methods based on local sets: A comparative study with several approaches from a bi-objective perspective

作者：

Highlights：

• We propose three selection strategies with different accuracy–reduction tradeoff.

• We assess them on 26 known databases with more than 1000 instances each one.

• The results are compared with those of 11 successful state-of-the-art methods.

• According to different criteria, the new methods are always among the top performers.

摘要

Highlights•We propose three selection strategies with different accuracy–reduction tradeoff.•We assess them on 26 known databases with more than 1000 instances each one.•The results are compared with those of 11 successful state-of-the-art methods.•According to different criteria, the new methods are always among the top performers.

论文关键词：Local sets,Instance selection,Data reduction,Prototype-based classifiers,Instance-based learning

论文评审过程：Received 21 November 2013, Revised 22 September 2014, Accepted 1 October 2014, Available online 12 October 2014.

论文官网地址：https://doi.org/10.1016/j.patcog.2014.10.001