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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73(11):4773–4795
Abualigah LM, Khader AT, Al-Betar MA, Alomari OA (2017a) Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering. Expert Syst Appl 84:24–36
Abualigah LM, Khader AT, Hanandeh ES, Gandomi AH (2017b) A novel hybridization strategy for krill herd algorithm applied to clustering techniques. Appl Soft Comput 60:423–435
Abualigah LM, Khader AT, Hanandeh ES (2018a) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48(11):4047–4071
Abualigah LM, Khader AT, Hanandeh ES (2018b) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci 25:456–466
Cai J, Luo J, Wang S, Yang S (2018) Feature selection in machine learning: a new perspective. Neurocomputing 300:70–79
Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng 40:16–28
Hamidzadeh J, Sadeghi R, Namaei N (2017) Weighted support vector data description based on chaotic bat algorithm. Appl Soft Comput 60:540–551
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: IEEE international conference on neural networks, vol 4, pp 1942–1948
Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: 1997 IEEE international conference on systems, man, and cybernetics. Computational Cybernetics and Simulation, pp 4104–4109
Khanzode VV, Maiti J (2008) Implementing Mahalanobis–Taguchi system to improve casting quality in grey iron foundry. Int J Prod Qual Manag 3(4):444–456
Kirch W (ed) (2008) Wilcoxon matched pairs signed rank test. Springer, Netherlands, pp 1460–1460
Mirjalili S, Hashim SZM (2010) A new hybrid PSOGSA algorithm for function optimization. In: 2010 international conference on computer and information application, pp 374–377
Mirjalili S, Wang GG, Coelho LS (2014) Binary optimization using hybrid particle swarm optimization and gravitational search algorithm. Neural Comput Appl 25(6):1423–1435
Navarro-Acosta JA, Reséndiz-Flores EO (2017) Gompertz binary particle swarm optimization and support vector data description system for fault detection and feature selection applied in automotive pedals components. Int J Adv Manuf Technol 92(5):2311–2324
Pal A, Maiti J (2010) Development of a hybrid methodology for dimensionality reduction in Mahalanobis–Taguchi system using Mahalanobis distance and binary particle swarm optimization. Expert Syst Appl 37(2):1286–1293
Panday D, de Amorim RC, Lane P (2018) Feature weighting as a tool for unsupervised feature selection. Inf Process Lett 129:44–52
Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248 (Special Section on High Order Fuzzy Sets)
Rashedi E, Nezamabadi-Pour H, Saryazdi S (2010) BGSA: binary gravitational search algorithm. Natural Comput 9(3):727–745
Reséndiz E, Moncayo-Martínez LA, Solís G (2013) Binary ant colony optimization applied to variable screening in the Mahalanobis–Taguchi system. Expert Syst Appl 40(2):634–637
Reséndiz-Flores E, López-Quintero M (2017) Optimal identification of impact variables in a welding process for automobile seats mechanism by MTS-GBPSO approach. Int J Adv Manuf Technol 90(1–4):437–443
Reséndiz-Flores EO, Navarro-Acosta JA, Mota-Gutiérrez CG, Reyes-Carlos YI (2018) Fault detection and optimal feature selection in automobile motor-head machining process. Int J Adv Manuf Technol 94(5):2613–2622
Reyes-Carlos YI, Mota-Gutiérrez CG, Reséndiz-Flores EO (2018) Optimal variable screening in automobile motor-head machining process using metaheuristic approaches in the Mahalanobis–Taguchi system. Int J Adv Manuf Technol 95(9):3589–3597
Rizal M, Ghani J, Nuawi M, Haron C (2017) Cutting tool wear classification and detection using multi-sensor signals and Mahalanobis-Taguchi system. Wear 376–377:1759–1765 (21st International Conference on Wear of Materials)
Sarhani M, Afia AE, Faizi R (2018) Facing the feature selection problem with a binary PSO-GSA approach. Springer, Berlin, pp 447–462
Stańczyk U, Jain LC (2015) Feature selection for data and pattern recognition. Springer, Berlin
Vignolo LD, Milone DH, Scharcanski J (2013) Feature selection for face recognition based on multi-objective evolutionary wrappers. Expert Syst Appl 40(13):5077–5084
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
This article does not contain any studies with humans or animals performed by any of the authors.
Additional information
Communicated by V. Loia.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Reséndiz-Flores, E.O., Navarro-Acosta, J.A. & Hernández-Martínez, A. Optimal feature selection in industrial foam injection processes using hybrid binary Particle Swarm Optimization and Gravitational Search Algorithm in the Mahalanobis–Taguchi System. Soft Comput 24, 341–349 (2020). https://doi.org/10.1007/s00500-019-03911-w
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-019-03911-w