Adjusting parameters of one-sided control chart-based support vector machines using genetic algorithm and particle swarm optimization
Abstract: 85
| 
PDF: 55 
##plugins.themes.academic_pro.article.main##
Author
Keywords:
Abstract
This study examines parameter optimization for one-sided control chart-based Support Vector Machines (SVM) using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Statistical learning classification effectively addresses statistical process control issues, especially in managing big data complexities. While SVM has computational benefits, its predictive accuracy relies on proper parameter selection. This research presents two methods—D-SVM-GA and D-SVM-PSO—to optimize key parameters like moving windows ( ), the Gamma (σ2), and the penalty cost (C). Results show upper control limits of 0.518 for D-SVM-GA and 0.515 for D-SVM-PSO, indicating effective optimization. The values highlight improved detection performance, where higher results in lower , enhancing responsiveness to shifts. These findings underscore the effectiveness of evolutionary algorithms in optimizing SVM parameters, offering insights for future applications in process control and statistical learning, with potential for improved accuracy and efficiency in data-driven decision-making.
References
-
[1] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. Berlin: Springer, 2009.
[2] F. K. Wang, B. Bizuneh, and C. Xiaobin, “One‐sided control chart based on support vector machines with differential evolution algorithm,” Quality and Reliability Engineering International, vol. 35, 2019, doi: 10.1002/qre.2465.
[3] F. K. Wang and T. Du, “Implementing support vector regression with differential evolution to forecast motherboard shipments,” Expert Systems with Applications, vol. 41, pp. 3850–3855, 2014, doi: 10.1016/j.eswa.2013.12.022.
[4] W. C. Hong, “Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model,” Energy Conversion and Management, vol. 50, pp. 105–117, 2009, doi: 10.1016/j.enconman.2008.08.031.
[5] S. He, W. Jiang, and H. Deng, “A distance-based control chart for monitoring multivariate processes using support vector machines,” Annals of Operations Research, vol. 263, no. 1, pp. 191–207, 2018, doi: 10.1007/s10479-016-2186-4.
[6] R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in Proc. 6th Int. Symp. Micro Machine and Human Science, IEEE, 1995, pp. 39–43.
[7] S. Du, J. Lv, and L. Xi, “On-line classifying process mean shifts in multivariate control charts based on multiclass support vector machines,” Int. J. Production Research, vol. 50, pp. 1–23, 2012, doi: 10.1080/00207543.2011.631596.
##plugins.themes.academic_pro.article.sidebar##
