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Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis

Citations

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Cited by:

  1. Ying Zhang & Mutahar Safdar & Jiarui Xie & Jinghao Li & Manuel Sage & Yaoyao Fiona Zhao, 2023. "A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3305-3340, December.
  2. Kigerl, Alex & Hamilton, Zachary & Kowalski, Melissa & Mei, Xiaohan, 2022. "The great methods bake-off: Comparing performance of machine learning algorithms," Journal of Criminal Justice, Elsevier, vol. 82(C).
  3. Koffi Dumor & Li Yao, 2019. "Estimating China’s Trade with Its Partner Countries within the Belt and Road Initiative Using Neural Network Analysis," Sustainability, MDPI, vol. 11(5), pages 1-22, March.
  4. Haoying Wang & Guohui Wu, 2022. "Modeling discrete choices with large fine-scale spatial data: opportunities and challenges," Journal of Geographical Systems, Springer, vol. 24(3), pages 325-351, July.
  5. Melvin Wong & Bilal Farooq, 2019. "Information processing constraints in travel behaviour modelling: A generative learning approach," Papers 1907.07036, arXiv.org, revised Jul 2019.
  6. Venkatraj, V. & Dixit, M.K., 2022. "Challenges in implementing data-driven approaches for building life cycle energy assessment: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
  7. Mahmoud Abdel-Sattar & Adel M. Al-Saif & Abdulwahed M. Aboukarima & Dalia H. Eshra & Lidia Sas-Paszt, 2022. "Quality Attributes Prediction of Flame Seedless Grape Clusters Based on Nutritional Status Employing Multiple Linear Regression Technique," Agriculture, MDPI, vol. 12(9), pages 1-19, August.
  8. Smeele, Nicholas V.R. & Chorus, Caspar G. & Schermer, Maartje H.N. & de Bekker-Grob, Esther W., 2023. "Towards machine learning for moral choice analysis in health economics: A literature review and research agenda," Social Science & Medicine, Elsevier, vol. 326(C).
  9. Horvath, Sabine & Soot, Matthias & Zaddach, Sebastian & Neuner, Hans & Weitkamp, Alexandra, 2021. "Deriving adequate sample sizes for ANN-based modelling of real estate valuation tasks by complexity analysis," Land Use Policy, Elsevier, vol. 107(C).
  10. Sophia Voulgaropoulou & Nikolaos Samaras & Nikolaos Ploskas, 2022. "Predicting the Execution Time of the Primal and Dual Simplex Algorithms Using Artificial Neural Networks," Mathematics, MDPI, vol. 10(7), pages 1-21, March.
  11. Coqueret, Guillaume & Deguest, Romain, 2024. "Unexpected opportunities in misspecified predictive regressions," European Journal of Operational Research, Elsevier, vol. 318(2), pages 686-700.
  12. Wang, Yuanping & Hou, Lingchun & Hu, Lang & Cai, Weiguang & Wang, Lin & Dai, Cuilian & Chen, Juntao, 2023. "How family structure type affects household energy consumption: A heterogeneous study based on Chinese household evidence," Energy, Elsevier, vol. 284(C).
  13. Oguzhan Essiz & Aysu Senyuz, 2024. "Predicting the value‐based determinants of sustainable luxury consumption: A multi‐analytical approach and pathway to sustainable development in the luxury industry," Business Strategy and the Environment, Wiley Blackwell, vol. 33(3), pages 1721-1758, March.
  14. Guillaume Coqueret & Romain Deguest, 2024. "Unexpected opportunities in misspecified predictive regressions," Post-Print hal-04595355, HAL.
  15. Shu-Long Luo & Xing Shi & Feng Yang, 2024. "A Review of Data-Driven Methods in Building Retrofit and Performance Optimization: From the Perspective of Carbon Emission Reductions," Energies, MDPI, vol. 17(18), pages 1-33, September.
  16. Mihai Mutascu & Scott W. Hegerty, 2023. "Predicting the contribution of artificial intelligence to unemployment rates: an artificial neural network approach," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(2), pages 400-416, June.
  17. Ester Vasta & Tommaso Scimone & Giovanni Nobile & Otto Eberhardt & Daniele Dugo & Massimiliano Maurizio De Benedetti & Luigi Lanuzza & Giuseppe Scarcella & Luca Patanè & Paolo Arena & Mario Cacciato, 2023. "Models for Battery Health Assessment: A Comparative Evaluation," Energies, MDPI, vol. 16(2), pages 1-34, January.
  18. Alwosheel, Ahmad & van Cranenburgh, Sander & Chorus, Caspar G., 2019. "‘Computer says no’ is not enough: Using prototypical examples to diagnose artificial neural networks for discrete choice analysis," Journal of choice modelling, Elsevier, vol. 33(C).
  19. Sadia Samar Ali & Rajbir Kaur & D. Jinil Persis & Raiswa Saha & Murugan Pattusamy & V. Raja Sreedharan, 2023. "Developing a hybrid evaluation approach for the low carbon performance on sustainable manufacturing environment," Annals of Operations Research, Springer, vol. 324(1), pages 249-281, May.
  20. Ibrahim A. Nafisah & Irsa Sajjad & Mohammed A. Alshahrani & Osama Abdulaziz Alamri & Mohammed M. A. Almazah & Javid Gani Dar, 2024. "Statistical Predictive Hybrid Choice Modeling: Exploring Embedded Neural Architecture," Mathematics, MDPI, vol. 12(19), pages 1-20, October.
  21. Melvin Wong & Bilal Farooq, 2019. "ResLogit: A residual neural network logit model for data-driven choice modelling," Papers 1912.10058, arXiv.org, revised Feb 2021.
  22. Salah Alghamdi & Waiching Tang & Sittimont Kanjanabootra & Dariusz Alterman, 2024. "Optimising Building Energy and Comfort Predictions with Intelligent Computational Model," Sustainability, MDPI, vol. 16(8), pages 1-18, April.
  23. Bonfiglio, A. & Camaioni, B. & Carta, V. & Cristiano, S., 2023. "Estimating the common agricultural policy milestones and targets by neural networks," Evaluation and Program Planning, Elsevier, vol. 99(C).
  24. Anis, Hassan T. & Kwon, Roy H., 2025. "End-to-end, decision-based, cardinality-constrained portfolio optimization," European Journal of Operational Research, Elsevier, vol. 320(3), pages 739-753.
  25. S. Van Cranenburgh & S. Wang & A. Vij & F. Pereira & J. Walker, 2021. "Choice modelling in the age of machine learning -- discussion paper," Papers 2101.11948, arXiv.org, revised Nov 2021.
  26. Sander Cranenburgh & Marco Kouwenhoven, 2021. "An artificial neural network based method to uncover the value-of-travel-time distribution," Transportation, Springer, vol. 48(5), pages 2545-2583, October.
  27. Shuangyang Lai & Haoming Chen & Yuexu Zhao, 2024. "Measurement and prediction of the development level of China’s digital economy," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
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