Ensemble learning by means of a multi-objective optimization design approach for dealing with imbalanced data sets
作者：
Highlights：
• A new taxonomy for multi-objective ensemble learning is proposed.
• A holistic study on multi-objective ensemble learning is performed.
• A collection of imbalanced data sets is used for comparison purposes.
• An imbalanced real-world drinking-water quality anomaly detection is solved.
• Results indicate the success of multi-objective ensemble learning.
摘要
•A new taxonomy for multi-objective ensemble learning is proposed.•A holistic study on multi-objective ensemble learning is performed.•A collection of imbalanced data sets is used for comparison purposes.•An imbalanced real-world drinking-water quality anomaly detection is solved.•Results indicate the success of multi-objective ensemble learning.
论文关键词：Ensemble learning,Multi-objective optimization,Imbalanced data sets
论文评审过程：Received 30 March 2019, Revised 25 December 2019, Accepted 21 January 2020, Available online 22 January 2020, Version of Record 28 January 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113232
