Efficient feature selection method using real-valued grasshopper optimization algorithm

作者：

Highlights：

• Grasshopper optimization algorithm is used to propose a feature selection method.

• Computation of feature goodness factor helps to find global solution.

• The accuracy of 100% is achieved by selecting 4 features out of 2308 in a dataset.

• The method achieves the highest classification accuracy in 7 out of 10 datasets.

摘要

•Grasshopper optimization algorithm is used to propose a feature selection method.•Computation of feature goodness factor helps to find global solution.•The accuracy of 100% is achieved by selecting 4 features out of 2308 in a dataset.•The method achieves the highest classification accuracy in 7 out of 10 datasets.

论文关键词：Feature selection,Grasshopper optimization algorithm,Meta-heuristic algorithms,Pattern recognition

论文评审过程：Received 26 April 2018, Revised 31 August 2018, Accepted 11 October 2018, Available online 12 October 2018, Version of Record 28 October 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.10.021