Binary dragonfly optimization for feature selection using time-varying transfer functions

作者：

Highlights：

• Novel feature selection approaches based on Binary Dragonfly Algorithm (BDA) are proposed.

• Eight time varying S-shaped and V-shaped transfer functions are proposed.

• The leverage of using time-varying transfer functions on exploration and exploitation behaviors is investigated.

• Extensive tests are made to assess the proposed algorithms on the datasets to prove their merits.

摘要

•Novel feature selection approaches based on Binary Dragonfly Algorithm (BDA) are proposed.•Eight time varying S-shaped and V-shaped transfer functions are proposed.•The leverage of using time-varying transfer functions on exploration and exploitation behaviors is investigated.•Extensive tests are made to assess the proposed algorithms on the datasets to prove their merits.

论文关键词：Feature selection,Optimization,Binary dragonfly algorithm,Classification,Transfer functions

论文评审过程：Received 28 December 2017, Revised 31 July 2018, Accepted 4 August 2018, Available online 6 August 2018, Version of Record 31 October 2018.

论文官网地址：https://doi.org/10.1016/j.knosys.2018.08.003