Ensemble feature selection in high dimension, low sample size datasets: Parallel and serial combination approaches
作者：
Highlights：
• Ensemble feature selection in high dimension, low sample size (HDLSS) data is investigated.
• Parallel and serial combination approaches for ensemble feature selection are compared.
• Three single baseline filter, wrapper, and embedded feature selection algorithms are used.
• Ensemble feature selection outperforms single feature selection for classification accuracy.
• The serial combination approach produces the largest feature reduction rate.
摘要
•Ensemble feature selection in high dimension, low sample size (HDLSS) data is investigated.•Parallel and serial combination approaches for ensemble feature selection are compared.•Three single baseline filter, wrapper, and embedded feature selection algorithms are used.•Ensemble feature selection outperforms single feature selection for classification accuracy.•The serial combination approach produces the largest feature reduction rate.
论文关键词：Data mining,Ensemble learning,Feature selection,High dimension low sample size,Machine learning
论文评审过程：Received 6 October 2019, Revised 12 May 2020, Accepted 30 May 2020, Available online 1 June 2020, Version of Record 5 June 2020.
论文官网地址：https://doi.org/10.1016/j.knosys.2020.106097
