政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/157914

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 116849/147881 (79%)
Visitors : 63824057 Online Users : 20

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

政大機構典藏 > 商學院 > 資訊管理學系 > 期刊論文 > Item 140.119/157914

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/157914

Title:	Reduce-then-predict or simultaneous reduce-and-predict? Data-driven sparse modeling for improving R&D Efficiency
Authors:	莊皓鈞 Chuang, Howard Hao-Chun;Hsiao, Pa-Chieh;Chou, Yen-Chun
Contributors:	資管系
Keywords:	Eye diagram;LASSO;machine learning;printed circuit boards;research and development;sparse principal component analysis (SPCA);unsupervised learning
Date:	2025-06
Issue Date:	2025-07-07 10:17:15 (UTC+8)
Abstract:	Efficient research and development (R&D) workflows are critical in industries where early-stage results influence downstream outcomes. This study develops a predictive model to enhance R&D efficiency for a leading integrated device manufacturer specializing in printed circuit board design. To address challenges of limited data, noise and collinearity, we apply sparse principal component analysis (SPCA) to simplify simulation data, followed by least absolute shrinkage and selection operator (LASSO) regression to predict later-stage physical testing performance. Our SPCA-LASSO model reduces prediction errors by 22%–41% compared to direct LASSO regression while offering interpretable insights for engineers. In contrast, sparse principal component regression, which integrates dimension reduction and prediction, yields higher errors and unstable factor loadings. This empirical comparison between reduce-then-predict and simultaneous reduce-and-predict approaches contributes to sparse modeling and engineering analytics, offering actionable insights for improving sequential R&D processes across high-tech industries, software engineering, construction, and other sectors where early performance predictions are critical.
Relation:	IEEE Transactions on Engineering Management, Vol.72, pp.2646-2660
Data Type:	article
DOI 連結:	https://doi.org/10.1109/TEM.2025.3577580
DOI:	10.1109/TEM.2025.3577580
Appears in Collections:	[資訊管理學系] 期刊論文

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	19	View/Open

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

Loading...