When is resampling beneficial for feature selection with imbalanced wide data?

作者：

Highlights：

• Wide datasets usually suffer from unbalanced classes distributions.

• Feature selection (FS) is commonly recommended for wide datasets.

• We aim to find the best combination and order to apply FS and resampling.

• 14 datasets, 5 classifiers, 7 FS, and 7 balancing strategies were tested.

• The best configuration was SVM-RFE used before RUS for the SVM-G classifier.

摘要

•Wide datasets usually suffer from unbalanced classes distributions.•Feature selection (FS) is commonly recommended for wide datasets.•We aim to find the best combination and order to apply FS and resampling.•14 datasets, 5 classifiers, 7 FS, and 7 balancing strategies were tested.•The best configuration was SVM-RFE used before RUS for the SVM-G classifier.

论文关键词：Feature selection,Wide data,High dimensional data,Very low sample size,Unbalanced,Machine learning

论文评审过程：Received 30 April 2021, Revised 30 July 2021, Accepted 30 September 2021, Available online 15 October 2021, Version of Record 20 October 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.116015