FW-SMOTE: A feature-weighted oversampling approach for imbalanced classification

作者：

Highlights：

• We claim that SMOTE has a weakness when facing high-dimensional problems.

• We propose a general version of the SMOTE strategy using OWA operators.

• The proposal includes a feature weighting process that considers relevancy/redundancy.

• This new component leads to a better definition of the neighborhood of minority samples.

• Experiments carried out on 42 datasets show the virtues of our method.

摘要

•We claim that SMOTE has a weakness when facing high-dimensional problems.•We propose a general version of the SMOTE strategy using OWA operators.•The proposal includes a feature weighting process that considers relevancy/redundancy.•This new component leads to a better definition of the neighborhood of minority samples.•Experiments carried out on 42 datasets show the virtues of our method.

论文关键词：Data resampling,SMOTE,OWA Operators,Feature selection,Imbalanced data classification

论文评审过程：Received 21 September 2020, Revised 17 December 2021, Accepted 22 December 2021, Available online 27 December 2021, Version of Record 4 January 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.108511