Support Vector Machines and Model Selection for Control Chart Pattern Recognition

Chih-Jen Su, I-Fei Chen, Tzong-Ru Tsai (), Tzu-Hsuan Wang and Yuhlong Lio
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Chih-Jen Su: Department of Management Sciences, Tamkang University, Tamsui District, New Taipei City 251301, Taiwan
I-Fei Chen: Department of Management Sciences, Tamkang University, Tamsui District, New Taipei City 251301, Taiwan
Tzong-Ru Tsai: Department of Statistics, Tamkang University, Tamsui District, New Taipei City 251301, Taiwan
Tzu-Hsuan Wang: Department of Statistics, Tamkang University, Tamsui District, New Taipei City 251301, Taiwan
Yuhlong Lio: Department of Mathematical Sciences, University of South Dakota, Vermillion, SD 57069, USA

Mathematics, 2025, vol. 13, issue 4, 1-17

Abstract: Resource-intensiveness often occurs in modern industrial settings; meanwhile, common issues and irregular patterns in production can lead to defects and variations in work-piece dimensions, negatively impacting products and increasing costs. Utilizing traditional process control charts to monitor the process and identify potential anomalies is expensive when intensive resources are needed. To conquer these downsides, algorithms for control chart pattern recognition (CCPR) leverage machine learning models to detect non-normality or normality and ensure product quality is established, and novel approaches that integrate the support vector machine (SVM), random forest (RF), and K-nearest neighbors (KNN) methods with the model selection criterion, named SVM-, RF-, and KNN-CCPR, respectively, are proposed. The three CCPR approaches can save sample resources in the initial process monitoring, improve the weak learner’s ability to recognize non-normal data, and include normality as a special case. Simulation results and case studies show that the proposed SVM-CCPR method outperforms the other two competitors with the highest recognition rate and yields favorable performance for quality control.

Keywords: K-nearest neighbors; Monte Carlo simulation; pattern recognition; random forest; support vector machines (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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