Hybrid Variable Selection and Support Vector Regression for Gas Sensor Optimization

Abstract

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Novel Improved Salp Swarm Algorithm: An Application for Feature Selection

“…The two most popular evolutionary computation approaches in feature selection are genetic algorithms (GAs) and particle swarm optimization (PSO), and for both there is an increasing trend in the number of studies using them in the last couple of decades [29]. They are both applied in wrapper approaches beside various classification algorithms, like support vector machines [31][32][33], K-nearest neighbor [34][35][36], artificial neural networks [37,38], decision tree [39] and so forth.…”

“…In [31], a regression real-world task regarding combustion processes in industry is considered, where support vector regression is actually employed for getting an optimal carbon monoxide concentration in the exhaust gases based on other characteristics. Besides a GA for feature selection, two more methods from Bayesian statistics are tested, but the GA approach proves to be superior.…”