Ensemble feature selection: Homogeneous and heterogeneous approaches

作者：

Highlights：

• Over the last years, ensemble learning has been the focus of much attention.

• We apply two different designs of ensemble learning on the feature selection process.

• Homogeneous ensemble distributes the dataset on different nodes.

• Heterogeneous ensemble combines the result of different feature selection methods.

• We reduce the training time and release the user to choose a feature selection method.

摘要

•Over the last years, ensemble learning has been the focus of much attention.•We apply two different designs of ensemble learning on the feature selection process.•Homogeneous ensemble distributes the dataset on different nodes.•Heterogeneous ensemble combines the result of different feature selection methods.•We reduce the training time and release the user to choose a feature selection method.

论文关键词：Ensemble learning,Feature selection,Ranking aggregation,Classification,SVM-Rank,Data complexity measures

论文评审过程：Received 3 June 2016, Revised 18 November 2016, Accepted 19 November 2016, Available online 27 November 2016, Version of Record 12 January 2017.

论文官网地址：https://doi.org/10.1016/j.knosys.2016.11.017