Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm

作者：

Highlights：

• A two-archive-guided multiobjective artificial bee colony algorithm was designed.

• The algorithm’s convergence and exploitation abilities are enhanced.

• Two archives are employed to enhance the search capability of the algorithm.

• Results have shown that TMABC-FS is an efficient and robust optimization method.

摘要

•A two-archive-guided multiobjective artificial bee colony algorithm was designed.•The algorithm’s convergence and exploitation abilities are enhanced.•Two archives are employed to enhance the search capability of the algorithm.•Results have shown that TMABC-FS is an efficient and robust optimization method.

论文关键词：Cost-sensitive feature selection,Artificial bee colony algorithm,Multi-objective optimization,Particle swarm optimization,Differential evolution

论文评审过程：Received 23 December 2018, Revised 21 June 2019, Accepted 22 June 2019, Available online 28 June 2019, Version of Record 4 July 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.06.044