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


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    題名: Reduce-then-predict or simultaneous reduce-and-predict? Data-driven sparse modeling for improving R&D Efficiency
    作者: 莊皓鈞
    Chuang, Howard Hao-Chun;Hsiao, Pa-Chieh;Chou, Yen-Chun
    貢獻者: 資管系
    關鍵詞: Eye diagram;LASSO;machine learning;printed circuit boards;research and development;sparse principal component analysis (SPCA);unsupervised learning
    日期: 2025-06
    上傳時間: 2025-07-07 10:17:15 (UTC+8)
    摘要: 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.
    關聯: IEEE Transactions on Engineering Management, Vol.72, pp.2646-2660
    資料類型: article
    DOI 連結: https://doi.org/10.1109/TEM.2025.3577580
    DOI: 10.1109/TEM.2025.3577580
    顯示於類別:[資訊管理學系] 期刊論文

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