Feature selection using Forest Optimization Algorithm

作者：

Highlights：

• FOA is adjusted for solving Feature Selection (FS) as a discrete search problem.

• The proposed FSFOA is compared with GA, PSO and ACO based methods.

• We investigated the performance of FSFOA on 11 well-known datasets from UCI.

• Results show improvement in classification accuracy of classifiers in some datasets.

摘要

Highlights•FOA is adjusted for solving Feature Selection (FS) as a discrete search problem.•The proposed FSFOA is compared with GA, PSO and ACO based methods.•We investigated the performance of FSFOA on 11 well-known datasets from UCI.•Results show improvement in classification accuracy of classifiers in some datasets.

论文关键词：Feature selection,Forest Optimization Algorithm (FOA),KNN classifier,Dimension reduction,FSFOA

论文评审过程：Received 17 August 2015, Revised 26 March 2016, Accepted 11 May 2016, Available online 24 May 2016, Version of Record 2 June 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.05.012