ATISA: Adaptive Threshold-based Instance Selection Algorithm

作者：

Highlights：

• We propose three instance reduction techniques called ATISA1,2,3.

• ATISA maintain important border and inner points per class based on an adaptive threshold.

• When compared with the state-of-the-art algorithms, ATISA1 obtains better accuracy rates and promising reduction rates.

• ATISA is faster than DROP3, ICF and HMN-EI.

摘要

•We propose three instance reduction techniques called ATISA1,2,3.•ATISA maintain important border and inner points per class based on an adaptive threshold.•When compared with the state-of-the-art algorithms, ATISA1 obtains better accuracy rates and promising reduction rates.•ATISA is faster than DROP3, ICF and HMN-EI.

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

论文评审过程：Available online 28 June 2013.

论文官网地址：https://doi.org/10.1016/j.eswa.2013.06.053