A Novel Feature Selection Method Based on Extreme Learning Machine and Fractional-Order Darwinian PSO. (6th May 2018)

• Record Type: Journal Article
• Authors: Wang, Yuan-Yuan; Zhang, Huan; Qiu, Chen-Hui; Xia, Shun-Ren
• Other Names: Gutierrez Pedro Antonio Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2018(2018)
• Publication Date: 2018-05-06
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2018/5078268
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : The paper presents a novel approach for feature selection based on extreme learning machine (ELM) and Fractional-order Darwinian particle swarm optimization (FODPSO) for regression problems. The proposed method constructs a fitness function by calculating mean square error (MSE) acquired from ELM. And the optimal solution of the fitness function is searched by an improved particle swarm optimization, FODPSO. In order to evaluate the performance of the proposed method, comparative experiments with other relative methods are conducted in seven public datasets. The proposed method obtains six lowest MSE values among all the comparative methods. Experimental results demonstrate that the proposed method has the superiority of getting lower MSE with the same scale of feature subset or requiring smaller scale of feature subset for similar MSE.