Fair-AdaBoost: Extending AdaBoost method to achieve fair classification

作者：

Highlights：

• A novel Fair-AdaBoost method is proposed to achieve fair classification.

• The NSGA-II algorithm is extended to enhance the Fair-AdaBoost.

• The Fair-AdaBoost outperforms the baseline methods in fair classification.

• The Fair-AdaBoost enhanced with HNSGA-II outperforms the benchmark methods.

摘要

•A novel Fair-AdaBoost method is proposed to achieve fair classification.•The NSGA-II algorithm is extended to enhance the Fair-AdaBoost.•The Fair-AdaBoost outperforms the baseline methods in fair classification.•The Fair-AdaBoost enhanced with HNSGA-II outperforms the benchmark methods.

论文关键词：Fair classification,AdaBoost,Machine learning,Unfairness mitigation

论文评审过程：Received 24 April 2021, Revised 3 February 2022, Accepted 10 April 2022, Available online 21 April 2022, Version of Record 5 May 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117240