Online feature importance ranking based on sensitivity analysis

作者：

Highlights：

• A feature ranking method is developed based on an importance measure.

• A feature's importance is calculated based on its impact on the class prediction.

• The adaptations for correlated and dynamic feature spaces are presented.

• The experimental results suggest better results compared with other methods.

摘要

•A feature ranking method is developed based on an importance measure.•A feature's importance is calculated based on its impact on the class prediction.•The adaptations for correlated and dynamic feature spaces are presented.•The experimental results suggest better results compared with other methods.

论文关键词：Feature ranking,Online learning,Stochastic gradient descent,Sensitivity

论文评审过程：Received 12 November 2016, Revised 5 May 2017, Accepted 6 May 2017, Available online 8 May 2017, Version of Record 6 June 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.05.016