Early stopping aggregation in selective variable selection ensembles for high-dimensional linear regression models
作者：
Highlights：
• A novel algorithm is devised to get more effective variable selection ensembles.
• ST2E is extended to high-dimensional cases by an effective prescreening step.
• A subensemble is attained by sorting members and then fusing some top-ranked ones.
• The accuracy-diversity patterns of both full and pruned subensembles are studied.
• Higher ranking and selection accuracies are gained by a pruned ensemble.
摘要
•A novel algorithm is devised to get more effective variable selection ensembles.•ST2E is extended to high-dimensional cases by an effective prescreening step.•A subensemble is attained by sorting members and then fusing some top-ranked ones.•The accuracy-diversity patterns of both full and pruned subensembles are studied.•Higher ranking and selection accuracies are gained by a pruned ensemble.
论文关键词：Variable selection ensemble,Ensemble pruning,Variable selection,Selection accuracy,Aggregation order,Ranking accuracy
论文评审过程：Received 3 November 2017, Revised 2 April 2018, Accepted 11 April 2018, Available online 14 April 2018, Version of Record 11 May 2018.
论文官网地址：https://doi.org/10.1016/j.knosys.2018.04.016
