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Soft Computing

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13.03.2019 | Methodologies and Application

Optimal feature selection in industrial foam injection processes using hybrid binary Particle Swarm Optimization and Gravitational Search Algorithm in the Mahalanobis–Taguchi System

verfasst von: Edgar O. Reséndiz-Flores, Jesús Alejandro Navarro-Acosta, Agustín Hernández-Martínez

Erschienen in: Soft Computing | Ausgabe 1/2020

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Abstract

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.

Vorheriger Artikel Spatial-domain steganalytic feature selection based on three-way interaction information and KS test

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Titel: Optimal feature selection in industrial foam injection processes using hybrid binary Particle Swarm Optimization and Gravitational Search Algorithm in the Mahalanobis–Taguchi System
verfasst von: Edgar O. Reséndiz-Flores
Jesús Alejandro Navarro-Acosta
Agustín Hernández-Martínez
Publikationsdatum: 13.03.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 1/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-019-03911-w

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