採迴歸樹進行規則探勘以有效同時降低多種紡織瑕疵

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姓名

楊華升(Hua-Sheng Yang) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

採迴歸樹進行規則探勘以有效同時降低多種紡織瑕疵
(Adopting regression tree for rules mining to effectively reduce various fabric defects simultaneously)

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摘要(中)

紡織業為臺灣不可或缺的產業，如今生產製程遭遇快速反應、品質穩定、交期掌控的三大關鍵挑戰，在機臺的參數設定階段找出瑕疵率較低的最佳參數範圍組合是維持品質穩定的策略之一。規則探勘為本研究鎖定重點，資料庫中瑕疵總數排名前四的瑕疵種類，針對其相關織物性質與機器參數進行數據分析，研究目的在於找出對各類瑕疵值最具影響力的重要特徵集，並將得出的特徵整併成範圍規則型式，最終各瑕疵種類規則需依照特定方式進行合併，旨在有效同時降低四種瑕疵並帶來全域效益，效益意即相較原始無設定此規則時減少多少總瑕疵比例。
實驗綜合多樣分析，包含採取不同特徵選取的[criterion, scoring](以中括號代表特徵選取所需的參數對，其餘處亦採取此表達格式)、制定自決策樹提煉候選規則的機制、擴大或縮小候選規則的參數範圍、合併規則的組合方式等。採用嵌套交叉驗證後，最終在各時間拆分點皆可探勘出效益大於等於0.8，或者支持度大於等於0.5或近似0.5的全域規則。支持度用來評估規則涵蓋測試資料的多寡，亦可解釋為規則對機器狀態是否穩定的影響程度。

摘要(英)

The textile industry is an indispensable industry in Taiwan. Today, the production process encounters three key challenges: rapid response, stable quality, and delivery control. Finding the best parameter range combination with a lower defect rate in the parameter setting stage of the machine is the key to maintaining stable quality. Rule mining is the focus of this research. The total number of defects in the database ranks in the top four types of defects, and data analysis is carried out according to their related fabric properties and machine parameters. The purpose of the research is to find out the most influential feature set for each type of defect value, and integrate the obtained features into a rule type. In the end, the rules for each type of flaws need to be combined in a specific way, aiming to effectively reduce the four flaws at the same time and bring global benefits. How much to reduce the percentage of total imperfections.
The experiment comprehensively analyzes various aspects, including the selection of different features [criterion, scoring], the development of a self-decision tree mechanism to extract candidate rules, the expansion or reduction of the parameter range of candidate rules, and the combination of merging rules, etc. After using nested cross-validation, the global rules with benefit greater than or equal to 0.8, or support greater than or equal to 0.5 or approximately 0.5 can be mined at each time split point. The support degree is used to evaluate the amount of test data covered by the rule, and it can also be interpreted as the degree of influence of the rule on the stability of the machine state.

關鍵字(中)

★ 參數組合
★ 規則探勘
★ 特徵選取
★ 決策樹

關鍵字(英)

★ parameter combination
★ rule mining
★ feature selection
★ decision tree

論文目次

中文摘要 i
Abstract ii
目錄 iii
圖目錄 vi
表目錄 viii
一、緒論 1
1-1 研究背景 1
1-2 研究動機與目的 2
1-3 研究貢獻 2
1-4 論文架構 3
二、相關研究 4
2-1 多目標最佳化 4
2-2 時間序列與嵌套交叉驗證 6
2-3 決策樹特性 8
三、解決方案 10
3-1 資料前處理 11
3-1-1 十碼瑕疵數 13
3-2 特徵選取 13
3-2-1 SFS(循序向前選擇法) 14
3-2-2 SBS(循序向後選擇法) 14
3-2-3 SFFS(循序浮動向前選擇法) 14
3-3 自迴歸樹提煉出候選規則 15
3-4 四種紡織瑕疵合併規則方式 17
3-5 問題定義 18
四、實驗設計與結果 20
4-1 整體實驗設計架構 20
4-2 評估規則的效益與支持度 21
4-3 實驗一: 嵌套交叉驗證和滾動原點 22
4-3-1 實驗動機與目的 22
4-3-2 實驗方法 24
4-3-3 實驗結果 25
4-4 實驗二: 對特徵選取挑選合適的[criterion, scoring] 25
4-4-1 解析實驗一效益異常現象原因 25
4-4-2 分析選定的單一特徵案例 26
4-4-2-1 實驗動機與目的 26
4-4-2-2 實驗方法 26
4-4-2-3 實驗結果 27
4-4-2-4 檢視測試樣本瑕疵數的資料分布 27
4-4-3 使用mse取代mae 28
4-4-3-1 實驗動機與目的 28
4-4-3-2 實驗方法 29
4-4-3-3 實驗結果 31
4-4-4 採用max_error評分 32
4-4-4-1 實驗動機與目的 32
4-4-4-2 實驗方法 32
4-4-4-3 實驗結果 32
4-4-5 實驗總結 33
4-4-5-1 使用實驗二找出的方法探勘規則 34
4-5 實驗三: 將候選規則做簡易一般化 36
4-5-1 實驗動機與目的 36
4-5-2 實驗方法 37
4-5-2-1 從葉節點朝向根節點刪除特徵 38
4-5-2-2 擴大或縮小規則範圍任意次數 38
4-5-3 實驗結果 39
4-6 實驗四: 調整提煉候選規則機制並嚴謹地過濾規則 42
4-6-1 實驗動機與目的 42
4-6-2 實驗方法 43
4-6-3 實驗結果 44
4-7 彙整所有實驗各時間點效益優良結果 54
五、結論與未來展望 59
5-1 結論 59
5-2 未來展望 60
參考文獻 61

參考文獻

[1] 中華民國紡織業拓展會: 2021台灣紡織工業概況, 2022-04-25, 取自
https://www.textiles.org.tw/ttf/main/content/ContentDesc.aspx?menu_id=95
[2] 財團法人資訊工業策進會王建敏總監: 紡織業製程數位化--品質與交期的改善策略, 2019-07-11, 取自
https://www.filaweaving.org.tw/show/show-680995.htm
[3] T. Yang, H. C. Lin, and M. L. Chen, "Metamodeling approach in solving the machine parameters optimization problem using neural network and genetic algorithms: A case study," Robot. Comput. Integr. Manuf., vol. 22, no. 4, pp. 322–331, 2006, doi: 10.1016/j.rcim.2005.07.004.
[4] J. Wu, C.-H. Zhang, and N.-X. C. C, "PSO algorithm-based parameter optimization for HEV powertrain and its control strategy," Int. J. Automot. Technol., vol. 9, no. 1, pp. 53–59, 2008, doi: 10.1007/s12239?008?0007?8.
[5] A. Fallah, E. Jabbari, and R. Babaee, "Development of the Kansa method for solving seepage problems using a new algorithm for the shape parameter optimization," Comput. Math. with Appl., vol. 77, no. 3, pp. 815–829, 2019, doi: 10.1016/j.camwa.2018.10.021.
[6] A. A. Almetwally, "Multi-objective Optimization of Woven Fabric Parameters Using Taguchi–Grey Relational Analysis," J. Nat. Fibers, vol. 17, no. 10, pp. 1468–1478, 2020, doi: 10.1080/15440478.2019.1579156.
[7] M. Maqsood, T. Hussain, N. Ahmad, and Y. Nawab, "Multi-response optimization of mechanical and comfort properties of bi-stretch woven fabrics using grey relational analysis in Taguchi method," J. Text. Inst., vol. 108, no. 5, pp. 794–802, 2017, doi: 10.1080/00405000.2016.1191721.
[8] R. Masaeli, H. Hasani, and M. Shanbeh, "Optimizing the physical properties of elastic-woven fabrics using Grey–Taguchi method," J. Text. Inst., vol. 106, no. 8, pp. 814–822, 2015, doi: 10.1080/00405000.2014.946341.
[9] A. Majumdar, S. P. Singh, and S. A. Ghosh, "Modelling, optimization and decision making techniques in designing of functional clothing," Indian J. Fibre Text. Res., vol. 36, no. 4, pp. 398–409, 2011.
[10] T. Ghosh and K. Martinsen, "Deep-learning assisted iterative multi-objective optimisation of yarn production process," Int. J. Exp. Des. Process Optim., vol. 6, no. 3, pp. 234–252, 2020, doi: 10.1504/IJEDPO.2020.113558.
[11] E. GilPavas and S. Correa-Sánchez, "Optimization of the heterogeneous electro-Fenton process assisted by scrap zero-valent iron for treating textile wastewater: Assessment of toxicity and biodegradability", J. Water Process Eng., vol. 32, no. August, pp. 1–12, 2019, doi: 10.1016/j.jwpe.2019.100924.
[12] 陳奕廷, "A Method and System for Extracting Key Factors Causing Defects Using Combining Sequential Backward Selection Method and Regression Tree Analysis: Taking Textile Manufacturing Process As an Example"，國立中央大學，碩士論文，2020-06
[13] TEXHR紡織人才網: 紡織品常見疵點織造原因分析, 2018-08-02, 取自
https://www.xuehua.us/a/5ec05afe719b7b73cc6b2a70?lang=zh-tw
[14] Mukhopadhyay, Arunangshu, Vinay Kumar Midha, and Nemai Chandra Ray, "Multi-objective optimization of parametric combination of injected slub yarn for producing knitted and woven fabrics with least abrasive damage." Research Journal of Textile and Apparel (2017).
[15] Chakraborty, Shankar, and Sunny Diyaley. "Multi-objective optimization of yarn characteristics using evolutionary algorithms: a comparative study." Journal of The Institution of Engineers (India): Series E 99.2 (2018): 129-140.
[16] Diyaley, Sunny, and Shankar Chakraborty. "Teaching-learning-based optimization of ring and rotor spinning processes." Soft Computing 25.15 (2021): 10287-10307.
[17] Bergmeir, Christoph, Rob J. Hyndman, and Bonsoo Koo. "A note on the validity of cross-validation for evaluating autoregressive time series prediction." Computational Statistics & Data Analysis 120 (2018): 70-83.
[18] Tashman, Leonard J. "Out-of-sample tests of forecasting accuracy: an analysis and review." International journal of forecasting 16.4 (2000): 437-450.
[19] Parvandeh, Saeid, et al. "Consensus features nested cross-validation." Bioinformatics 36.10 (2020): 3093-3098.
[20] Abdulaal, Mohammed J., Alexander J. Casson, and Patrick Gaydecki. "Performance of nested vs. Non-nested SVM cross-validation methods in visual BCI: Validation study." 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018.
[21] Cawley, Gavin C., and Nicola LC Talbot. "On over-fitting in model selection and subsequent selection bias in performance evaluation." The Journal of Machine Learning Research 11 (2010): 2079-2107.
[22] CASPER HANSEN: Nested Cross-Validation Python Code, 2019-05-30, 取自
https://mlfromscratch.com/nested-cross-validation-python-code/#/
[23] Kuhn, Max, and Kjell Johnson. Applied predictive modeling. Vol. 26. New York: Springer, 2013.
[24] Doshi-Velez, Finale, and Been Kim. "Towards a rigorous science of interpretable machine learning." arXiv preprint arXiv:1702.08608 (2017).
[25] Garcia, Isel Del Carmen Grau, et al. "Grey-Box Model: An ensemble approach for addressing semi-supervised classification problems." 25th Belgian-Dutch Conference on Machine Learning. 2016.
[26] Robnik-Šikonja, Marko, and Igor Kononenko. "Explaining classifications for individual instances." IEEE Transactions on Knowledge and Data Engineering 20.5 (2008): 589-600.
[27] Christoph Molnar, Interpretable Machine Learning, 2022-03-29, 取自
https://christophm.github.io/interpretable-ml-book/
[28] Jason Brownlee, Parametric and Nonparametric Machine Learning Algorithms, 2016-03-14, 取自
https://machinelearningmastery.com/parametric-and-nonparametric-machine-learning-algorithms/
[29] Arif R, Regression in Decision Tree — A Step by Step CART (Classification And Regression Tree), 2020-05-03, 取自
https://arifromadhan19.medium.com/regrssion-in-decision-tree-a-step-by-step-cart-classification-and-regression-tree-196c6ac9711e
[30] Safavian, S. Rasoul, and David Landgrebe. "A survey of decision tree classifier methodology." IEEE transactions on systems, man, and cybernetics 21.3 (1991): 660-674.
[31] sklearn.tree.DecisionTreeRegressor,
https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html
[32] MBA智庫百科, 織物密度, 取自
https://wiki.mbalib.com/zh-tw/%E7%BB%87%E7%89%A9%E5%AF%86%E5%BA%A6
[33] Drotár, Peter, Juraj Gazda, and Zdenek Smékal. "An experimental comparison of feature selection methods on two-class biomedical datasets." Computers in biology and medicine 66 (2015): 1-10.
[34] Khaire, Utkarsh Mahadeo, and R. Dhanalakshmi. "Stability of feature selection algorithm: A review." Journal of King Saud University-Computer and Information Sciences (2019).

[35] Dunne, Kevin, Padraig Cunningham, and Francisco Azuaje. "Solutions to instability problems with sequential wrapper-based approaches to feature selection." Journal of Machine Learning Research 1 (2002): 22.
[36] Hoque, Nazrul, Dhruba K. Bhattacharyya, and Jugal K. Kalita. "MIFS-ND: A mutual information-based feature selection method." Expert Systems with Applications 41.14 (2014): 6371-6385.
[37] Jović, Alan, Karla Brkić, and Nikola Bogunović. "A review of feature selection methods with applications." 2015 38th international convention on information and communication technology, electronics and microelectronics (MIPRO). Ieee, 2015.
[38] Naheed, Nazish, et al. "Importance of features selection, attributes selection, challenges and future directions for medical imaging data: a review." Computer Modeling in Engineering & Sciences 125.1 (2020): 314-344.
[39] Bouatmane, Sabrina, et al. "Round-Robin sequential forward selection algorithm for prostate cancer classification and diagnosis using multispectral imagery." Machine Vision and Applications 22.5 (2011): 865-878.
[40] Tsymbal, Alexey, et al. "Search strategies for ensemble feature selection in medical diagnostics." 16th IEEE Symposium Computer-Based Medical Systems, 2003. Proceedings.. IEEE, 2003.
[41] SequentialFeatureSelector: The popular forward and backward feature selection approaches incl. floating variants, 取自
http://rasbt.github.io/mlxtend/user_guide/feature_selection/SequentialFeatureSelector/
[42] Bemister-Buffington, Joseph, et al. "Machine Learning to Identify Flexibility Signatures of Class A GPCR Inhibition." Biomolecules 10.3 (2020): 454.
[43] Understanding the decision tree structure - Scikit-learn, 取自
https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html
[44] RakeshV, How is the MSE of each node in the DecisionTreeRegressor of scikit-learn calculated?, 2020-08-03, 取自
https://stackoverflow.com/questions/63144605/how-is-the-mse-of-each-node-in-the-decisiontreeregressor-of-scikit-learn-calcula
[45] Kim, Seong-Jun, and Kang Bae Lee. "Constructing decision trees with multiple response variables." International Journal of Management and Decision Making 4.4 (2003): 337-353.

指導教授

梁德容張欽圳(Deron Liang Chin-Chun Chang)

審核日期

2022-8-22

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