Optimal feature selection in industrial foam injection processes using hybrid binary Particle Swarm Optimization and Gravitational Search Algorithm in the Mahalanobis–Taguchi System
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The detection of variables that contribute to the variation of a system is one of the most important considerations in the industrial manufacturing processes. This work presents the combination of Mahalanobis–Taguchi system and a hybrid binary metaheuristic based on particle swarm optimization and gravitational search algorithm (BPSOGSA) to perform an optimal feature selection in order to detect the relevant variables in a real process of foam injection in automotive industry. The proposed method is compared with other feature selection approach based in binary PSO algorithm. The experimental results revealed that BPSOGSA is faster and successfully converge selecting a smallest subset of features than BPSO. Moreover, the feature selection effect is validated through other widely used machine learning algorithms which improve their accuracy performance when they are trained with the subset of detected variables by the proposed system.
KeywordsFeature selection Mahalanobis–Taguchi System Binary Particle Swarm Optimization Binary Gravitational Search Algorithm
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Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with humans or animals performed by any of the authors.
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