|| 中華民國紡織業拓展會: 2021台灣紡織工業概況, 2022-04-25, 取自|
 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
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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
 TEXHR紡織人才網: 紡織品常見疵點織造原因分析, 2018-08-02, 取自
 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).
 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.
 Diyaley, Sunny, and Shankar Chakraborty. "Teaching-learning-based optimization of ring and rotor spinning processes." Soft Computing 25.15 (2021): 10287-10307.
 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.
 Tashman, Leonard J. "Out-of-sample tests of forecasting accuracy: an analysis and review." International journal of forecasting 16.4 (2000): 437-450.
 Parvandeh, Saeid, et al. "Consensus features nested cross-validation." Bioinformatics 36.10 (2020): 3093-3098.
 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.
 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.
 CASPER HANSEN: Nested Cross-Validation Python Code, 2019-05-30, 取自
 Kuhn, Max, and Kjell Johnson. Applied predictive modeling. Vol. 26. New York: Springer, 2013.
 Doshi-Velez, Finale, and Been Kim. "Towards a rigorous science of interpretable machine learning." arXiv preprint arXiv:1702.08608 (2017).
 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.
 Robnik-Šikonja, Marko, and Igor Kononenko. "Explaining classifications for individual instances." IEEE Transactions on Knowledge and Data Engineering 20.5 (2008): 589-600.
 Christoph Molnar, Interpretable Machine Learning, 2022-03-29, 取自
 Jason Brownlee, Parametric and Nonparametric Machine Learning Algorithms, 2016-03-14, 取自
 Arif R, Regression in Decision Tree — A Step by Step CART (Classification And Regression Tree), 2020-05-03, 取自
 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.
 MBA智庫百科, 織物密度, 取自
 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.
 Khaire, Utkarsh Mahadeo, and R. Dhanalakshmi. "Stability of feature selection algorithm: A review." Journal of King Saud University-Computer and Information Sciences (2019).
 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.
 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.
 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.
 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.
 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.
 Tsymbal, Alexey, et al. "Search strategies for ensemble feature selection in medical diagnostics." 16th IEEE Symposium Computer-Based Medical Systems, 2003. Proceedings.. IEEE, 2003.
 SequentialFeatureSelector: The popular forward and backward feature selection approaches incl. floating variants, 取自
 Bemister-Buffington, Joseph, et al. "Machine Learning to Identify Flexibility Signatures of Class A GPCR Inhibition." Biomolecules 10.3 (2020): 454.
 Understanding the decision tree structure - Scikit-learn, 取自
 RakeshV, How is the MSE of each node in the DecisionTreeRegressor of scikit-learn calculated?, 2020-08-03, 取自
 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.