A Novel Feature Selection Method Based on Extreme Learning Machine and Fractional-Order Darwinian PSO

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. A...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2018-01, Vol.2018 (2018), p.1-8
Main Authors: Xia, Shun-Ren, Qiu, Chen-Hui, Zhang, Huan, Wang, Yuan-Yuan
Format: Article
Language:English
Subjects:
Artificial intelligence
Comparative analysis
Fitness
International conferences
Learning algorithms
Machine Learning
Mathematical optimization
Methods
Neural networks
Optimization algorithms
Particle swarm optimization
Population
Reproductive fitness
Velocity
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Summary: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.
ISSN:1687-5265
1687-5273
DOI:10.1155/2018/5078268