An improved Dragonfly Algorithm for feature selection

作者：

Highlights：

• This paper proposes an improved Binary Dragonfly Algorithm (BDA) for Feature Selection.

• Three versions of BDA are proposed: LBDA, QBDA, and SBDA .

• Eighteen datasets taken from UCI machine learning repository are used for evaluation process.

• SBDA excels LBDA and QBDA in terms of accuracy, number of features, and fitness function.

• SBDA excels the nine comparative methods in 12 out of 18 dataset in terms of accuracy.

摘要

•This paper proposes an improved Binary Dragonfly Algorithm (BDA) for Feature Selection.•Three versions of BDA are proposed: LBDA, QBDA, and SBDA .•Eighteen datasets taken from UCI machine learning repository are used for evaluation process.•SBDA excels LBDA and QBDA in terms of accuracy, number of features, and fitness function.•SBDA excels the nine comparative methods in 12 out of 18 dataset in terms of accuracy.

论文关键词：Feature selection,Binary Dragonfly Algorithm,Selected features,Classification accuracy,V-shaped transfer function,Optimization

论文评审过程：Received 14 December 2019, Revised 6 June 2020, Accepted 9 June 2020, Available online 19 June 2020, Version of Record 22 June 2020.

论文官网地址：https://doi.org/10.1016/j.knosys.2020.106131