Support vector machines and model selection for control chart pattern recognition

doi:10.3390/math13040592

淡江大學機構典藏 > 商管學院 > 統計學系暨研究所 > 期刊論文 > Item 987654321/127676

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/127676

Title:	Support vector machines and model selection for control chart pattern recognition
Authors:	Su, Chih-Jen;Chen, I-Fei;Tsai, Tzong-Ru;Wang, Tzu-Hsuan;Lio, Yuhlong
Keywords:	K-nearest neighbors;Monte Carlo simulation;pattern recognition;random forest;support vector machines
Date:	2025-02-11
Issue Date:	2025-09-08 12:05:10 (UTC+8)
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.
Relation:	Mathematics 13(4), p.592
DOI:	10.3390/math13040592
Appears in Collections:	[統計學系暨研究所] 期刊論文

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