Ensemble Computational Pipelines for Robust Machine Learning with Applications in Manufacturing

Published Online:https://doi.org/10.1287/ijds.2024.0052

References

  • Andersen MS, Dahl J, Vandenberghe L (2013) CVXOPT: A python package for convex optimization. Accessed November 26, 2025, https://cvxopt.org.Google Scholar
  • Aslanian E, Radmanesh M, Jalili M (2016) Hybrid recommender systems based on content feature relationship. IEEE Trans. Indust. Inform., ePub ahead of print November 21, https://doi.org/10.1109/TII.2016.2631138.Google Scholar
  • Cabot JH, Ross EG (2023) Evaluating prediction model performance. Surgery 174(3):723–726.Google Scholar
  • Chen X, Jin R (2018) Data fusion pipelines for autonomous smart manufacturing. Vogel-Heuser B, Fantuzzi C, eds. Proc. 2018 IEEE 14th Internat. Conf. Automation Sci. Engrg. (Institute of Electrical and Electronics Engineers, Piscataway, NJ), 1203–1208.Google Scholar
  • Chen X, Jin R (2021) AdaPipe: A recommender system for adaptive computation pipelines in cyber-manufacturing computation services. IEEE Trans. Indust. Inform. 17(9):6221–6229.Google Scholar
  • Chen X, Jin R (2024) Lori: Local low-rank response imputation for automatic configuration of contextualized artificial intelligence. IEEE Trans. Indust. Inform. 20(12):13707–13718. Google Scholar
  • Cormack GV, Clarke CLA, Buettcher S (2009) Reciprocal rank fusion outperforms Condorcet and individual rank learning methods. Proc. 32nd Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (Association for Computing Machinery, New York), 758–759.Google Scholar
  • Coscrato V, Bridge D (2023) Recommendation uncertainty in implicit feedback recommender systems. Longo L, O’Reilly R, eds. Artificial Intelligence and Cognitive Science (Springer Nature, Cham, Switzerland), 279–291.Google Scholar
  • Cruz YJ, Villalonga A, Castaño F, Rivas M, Haber RE (2024) Automated machine learning methodology for optimizing production processes in small and medium-sized enterprises. Oper. Res. Perspect. 12:100308. Google Scholar
  • Erickson N, Mueller J, Shirkov A, Zhang H, Larroy P, Li M, Smola A (2020) AutoGluon-Tabular: Robust and accurate AutoML for structured data. Preprint, submitted March 13, https://arxiv.org/abs/2003.06505. Google Scholar
  • Feurer M, Eggensperger K, Falkner S, Lindauer M, Hutter F (2022) Auto-sklearn 2.0: Hands-free AutoML via meta-learning. J. Machine Learn. Res. 23(1):261.Google Scholar
  • Fu M, Huang L, Rao A, Irissappane AA, Zhang J, Qu H (2023) A deep reinforcement learning recommender system with multiple policies for recommendations. IEEE Trans. Indust. Inform. 19(2):2049–2061.Google Scholar
  • Gupta D, Chopra N, Nair N, Sharma P (2020) Enhancing user experience with AI-powered recommendation engines: A comparative study of collaborative filtering, neural collaborative filtering, and matrix factorization algorithms. J. AI ML Res. 9(4).Google Scholar
  • He X, Liao L, Zhang H, Nie L, Hu X, Chua TS (2017) Neural collaborative filtering. Proc. 26th Internat. Conf. World Wide Web (Association for Computing Machinery, New York), 173–182.Google Scholar
  • Hoff PD, Raftery AE, Handcock MS (2002) Latent space approaches to social network analysis. J. Amer. Statist. Assoc. 97(460):1090–1098.Google Scholar
  • Jeschke S, Brecher C, Meisen T, Özdemir D, Eschert T (2017) Industrial internet of things and cyber manufacturing systems. Jeschke S, Brecher C, Song H, Rawat D, eds. Industrial Internet of Things (Springer, Cham, Switzerland), 3–19.Google Scholar
  • Kang S, Jin R, Deng X, Kenett RS (2023) Challenges of modeling and analysis in cybermanufacturing: A review from a machine learning and computation perspective. J. Intelligent Manufacturing 34(2):415–428.Google Scholar
  • Kim D, Li Q, Jang J, Kim DS, Kim J (2024) AXCF: Aspect-based collaborative filtering for explainable recommendations. Expert Systems 41(8):e13594.Google Scholar
  • Markowitz H (1952) Portfolio selection. J. Finance 7(1):77–91.Google Scholar
  • Markowitz HM (1959) Portfolio Selection: Efficient Diversification of Investments (Yale University Press, New Haven, CT), 350–368.Google Scholar
  • Masood SH (2014) Advances in fused deposition modeling. Hashmi S, Batalha GF, Van Tyne CJ, Yilbas B, eds. Comprehensive Materials Processing (Elsevier, Oxford, UK), 69–91.Google Scholar
  • Messaoudi F, Loukili M (2024) E-commerce personalized recommendations: A deep neural collaborative filtering approach. Oper. Res. Forum 5(1):5.Google Scholar
  • Nagarajah T, Poravi G (2019) A review on automated machine learning (AutoML) systems. 2019 IEEE Fifth Internat. Conf. Convergence Tech. (Institute of Electrical and Electronics Engineers, Piscataway, NJ), 1–6. Google Scholar
  • Neal RM (1993) Probabilistic inference using Markov chain Monte Carlo methods. Technical Report CRG-TR-93-1, Department of Computer Science, University of Toronto, Toronto.Google Scholar
  • Olson RS, Moore JH (2016) TPOT: A tree-based pipeline optimization tool for automating machine learning. Hutter F, Kotthoff L, Vanschoren J, eds. Proc. Workshop Automatic Machine Learn., Proceedings of Machine Learning Research, vol. 64 (PMLR, New York), 66–74. Google Scholar
  • Porteous I, Asuncion A, Welling M (2010) Bayesian matrix factorization with side information and Dirichlet process mixtures. Proc. AAAI Conf. Artificial Intelligence, vol. 24 (AAAI Press, Palo Alto, CA), 563–568.Google Scholar
  • Ren P, Xiao Y, Chang X, Huang PY, Li Z, Chen X, Wang X (2021) A comprehensive survey of neural architecture search: Challenges and solutions. ACM Comput. Surveys 54(4):1–34.Google Scholar
  • Salakhutdinov R, Mnih A (2008) Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. Proc. 25th Internat. Conf. Machine Learn. (Association for Computing Machinery, New York), 880–887.Google Scholar
  • Shojaee P, Zeng Y, Chen X, Jin R, Deng X, Zhang C (2021) Deep neural network pipelines for multivariate time series classification in smart manufacturing. 2021 Fourth IEEE Internat. Conf. Indust. Cyber-Physical Systems (Institute of Electrical and Electronics Engineers, Piscataway, NJ), 98–103.Google Scholar
  • Tecator (1992) Tecator meat spectroscopy dataset. Accessed November 30, 2023, https://lib.stat.cmu.edu/datasets/tecator.Google Scholar
  • Thornton C, Hutter F, Hoos HH, Leyton-Brown K (2013) Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. Proc. 19th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 847–855.Google Scholar
  • Wang XJ, Liu T, Fan W (2023) TGVx: Dynamic personalized POI deep recommendation model. INFORMS J. Comput. 35(4):786–796.LinkGoogle Scholar
  • Wang Y, Wang L, Li Y, He D, Liu TY (2013) A theoretical analysis of NDCG type ranking measures. Shalev-Shwartz S, Steinwart I, eds. Proc. 26th Annual Conf. Learn. Theory, Proceedings of Machine Learning Research, vol. 30 (PMLR, New York), 25–54.Google Scholar
  • Wilkinson NJ, Smith MAA, Kay RW, Harris RA (2019) A review of aerosol jet printing—A non-traditional hybrid process for micro-manufacturing. Internat. J. Advanced Manufacturing Tech. 105(11):4599–4619.Google Scholar
  • Zeng Y, Chen X, Jin R (2023) Ensemble active learning by contextual bandits for ai incubation in manufacturing. ACM Trans. Intelligent Systems Tech. 15(1):7.Google Scholar
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