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題名 以監督式學習方法進行檢驗管控
Quality Control by Supervised Learning Method作者 游景翔
Yu, Ching-Hsiang貢獻者 周珮婷<br>林怡伶
游景翔
Yu, Ching-Hsiang關鍵詞 監督式學習
品質成本
進料檢驗
特徵選取
Supervised learning
Quality cost
Incoming quality control
Feature selection日期 2018 上傳時間 4-Jul-2018 14:45:27 (UTC+8) 摘要 本研究之動機為將探討傳統的進料檢驗管控(Incoming Quality Control, IQC)之允收抽樣計畫之假設、特性以及允收過程，將其關鍵想法做為資料與變數模擬之依據，並藉由該模擬資料進行監督式機器學習模型之配適，預測材料或零件供應商所提供之抽驗資料是否具有造假之意圖。首先，本研究依照允收抽驗計畫的假設特性，將利用供應商抽到未符合標準公差之抽樣零件時即進行重新抽取樣本直至符合其標準的行為視為造假資料，並使用遞迴的方式進行模擬。再來，運用支持向量機、羅吉斯迴歸以及隨機森林等監督式學習方法進行預測，並比較各個變數的預測效果。從結果來看，依照允收抽驗樣本選擇的變數對於分辨供應商資料是否造假具有不錯的效果，依照本研究之結論，企業可依照供應商之抽驗資料轉換特性並建置供應商管理判別系統，並利用該方式作為供應商的選擇以及評估，其必可降低企業之鑑定成本(Appraisal Cost) ，造就供應商、零售商與客戶之間的三贏局面。
The purpose of the current study was to explore the assumptions, features, and acceptance process of acceptance sampling plan in traditional Incoming Quality Control (IQC).Four features were proposed to describe distributions of data. Supervised machine learning models, Support Vector Machine(SVM), Logistic Regression, and Random Forest, were applied for detection of fraud.The results showed that the proposed features can effectively differentiate between real and fake datasets. The techniques can be used in future for supplier selection and evaluation. The identification of appraisal cost will be reduced and a triple-win situation for suppliers, retailers, and customers can be created.參考文獻 參考文獻Alpaydin, E. (2010). Introduction to machine learning (2nd ed.). Cambridge, MA: MIT Press.Boulesteix A-L, Tutz G. (2006). Identification of interaction patterns and classification with applications to microarray data, Comput. Stat. Data Anal.Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.doi:10.1023/a:1010933404324Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3),273-297. doi:10.1007/bf00994018Do, T.-N., Lenca, P., Lallich, S., & Pham, N.-K. (2010). Classifying very-high-dimensional data with random forests of oblique decision trees. In F. Guil-let, G.Ritschard, D. Zighed, & H. Briand (Eds.), Advances in knowledge discovery and management. Berlin: Springer.Feigenbaum, A.V.(1961), Total Quality Control, New York, McGraw-Hill.Gosavi, S. S. (2014). Machine learning methods for fault classification .Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection.Journal of machine learning research, 3(Mar), 1157-1182.Juran, J.M (1951), Quality Control Handbook. McGraw-HillLee Rodgers, J., & Nicewander, W. A. (1988). Thirteen Ways to Look at theCorrelation Coefficient. The American Statistician, 42(1), 59-66.doi:10.1080/00031305.1988.10475524Ribeiro, B. (2005). Support vector machines for quality monitoring in a plastic injection molding process. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 35, 401–410. doi:10.1109/TSMCC.2004.843228CNS,「CNS 9445-計量值檢驗抽驗程式及抽驗表」,中國國家標準(1994)。 描述 碩士
國立政治大學
統計學系
105354022資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105354022 資料類型 thesis dc.contributor.advisor 周珮婷<br>林怡伶 zh_TW dc.contributor.author (Authors) 游景翔 zh_TW dc.contributor.author (Authors) Yu, Ching-Hsiang en_US dc.creator (作者) 游景翔 zh_TW dc.creator (作者) Yu, Ching-Hsiang en_US dc.date (日期) 2018 en_US dc.date.accessioned 4-Jul-2018 14:45:27 (UTC+8) - dc.date.available 4-Jul-2018 14:45:27 (UTC+8) - dc.date.issued (上傳時間) 4-Jul-2018 14:45:27 (UTC+8) - dc.identifier (Other Identifiers) G0105354022 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/118356 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 統計學系 zh_TW dc.description (描述) 105354022 zh_TW dc.description.abstract (摘要) 本研究之動機為將探討傳統的進料檢驗管控(Incoming Quality Control, IQC)之允收抽樣計畫之假設、特性以及允收過程，將其關鍵想法做為資料與變數模擬之依據，並藉由該模擬資料進行監督式機器學習模型之配適，預測材料或零件供應商所提供之抽驗資料是否具有造假之意圖。首先，本研究依照允收抽驗計畫的假設特性，將利用供應商抽到未符合標準公差之抽樣零件時即進行重新抽取樣本直至符合其標準的行為視為造假資料，並使用遞迴的方式進行模擬。再來，運用支持向量機、羅吉斯迴歸以及隨機森林等監督式學習方法進行預測，並比較各個變數的預測效果。從結果來看，依照允收抽驗樣本選擇的變數對於分辨供應商資料是否造假具有不錯的效果，依照本研究之結論，企業可依照供應商之抽驗資料轉換特性並建置供應商管理判別系統，並利用該方式作為供應商的選擇以及評估，其必可降低企業之鑑定成本(Appraisal Cost) ，造就供應商、零售商與客戶之間的三贏局面。 zh_TW dc.description.abstract (摘要) The purpose of the current study was to explore the assumptions, features, and acceptance process of acceptance sampling plan in traditional Incoming Quality Control (IQC).Four features were proposed to describe distributions of data. Supervised machine learning models, Support Vector Machine(SVM), Logistic Regression, and Random Forest, were applied for detection of fraud.The results showed that the proposed features can effectively differentiate between real and fake datasets. The techniques can be used in future for supplier selection and evaluation. The identification of appraisal cost will be reduced and a triple-win situation for suppliers, retailers, and customers can be created. en_US dc.description.tableofcontents 目次第壹章 緒論 1第一節 品質成本、供應商選擇與進料檢驗管控 1第二節 研究動機與目的 2第貳章 文獻探討 3第參章 研究方法及資料 5第一節 資料與變數模擬 5第二節 演算法模型 6第三節 特徵選取方法 10第肆章 資料分析與結果 12第一節 實驗步驟與分析 12第二節 演算法參數之過程與選擇 15第三節 特徵選取之結果與選擇 20第四節 實驗結果與方法比較 22第伍章 結論與建議 25第一節 結論 25第二節 研究限制、建議與未來展望 26參考文獻 27 zh_TW dc.format.extent 1371773 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105354022 en_US dc.subject (關鍵詞) 監督式學習 zh_TW dc.subject (關鍵詞) 品質成本 zh_TW dc.subject (關鍵詞) 進料檢驗 zh_TW dc.subject (關鍵詞) 特徵選取 zh_TW dc.subject (關鍵詞) Supervised learning en_US dc.subject (關鍵詞) Quality cost en_US dc.subject (關鍵詞) Incoming quality control en_US dc.subject (關鍵詞) Feature selection en_US dc.title (題名) 以監督式學習方法進行檢驗管控 zh_TW dc.title (題名) Quality Control by Supervised Learning Method en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 參考文獻Alpaydin, E. (2010). Introduction to machine learning (2nd ed.). Cambridge, MA: MIT Press.Boulesteix A-L, Tutz G. (2006). Identification of interaction patterns and classification with applications to microarray data, Comput. Stat. Data Anal.Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.doi:10.1023/a:1010933404324Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3),273-297. doi:10.1007/bf00994018Do, T.-N., Lenca, P., Lallich, S., & Pham, N.-K. (2010). Classifying very-high-dimensional data with random forests of oblique decision trees. In F. Guil-let, G.Ritschard, D. Zighed, & H. Briand (Eds.), Advances in knowledge discovery and management. Berlin: Springer.Feigenbaum, A.V.(1961), Total Quality Control, New York, McGraw-Hill.Gosavi, S. S. (2014). Machine learning methods for fault classification .Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection.Journal of machine learning research, 3(Mar), 1157-1182.Juran, J.M (1951), Quality Control Handbook. McGraw-HillLee Rodgers, J., & Nicewander, W. A. (1988). Thirteen Ways to Look at theCorrelation Coefficient. The American Statistician, 42(1), 59-66.doi:10.1080/00031305.1988.10475524Ribeiro, B. (2005). Support vector machines for quality monitoring in a plastic injection molding process. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 35, 401–410. doi:10.1109/TSMCC.2004.843228CNS,「CNS 9445-計量值檢驗抽驗程式及抽驗表」,中國國家標準(1994)。 zh_TW dc.identifier.doi (DOI) 10.6814/THE.NCCU.STAT.005.2018.B03 -
