Abstract
This paper describes an implementation of the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology for a demonstrative case of human queue waiting time prediction. We collaborated with a multiple domain (e.g., bank, pharmacies) ticket management service software development company, aiming to study a Machine Learning (ML) approach to estimate queue waiting time. A large multiple domain database was analyzed, which included millions of records related with two time periods (one year, for the modeling experiments; and two year, for a deployment simulation). The data was first preprocessed (including data cleaning and feature engineering tasks) and then modeled by exploring five state-of-the-art ML regression algorithms and four input attribute selections (including newly engineered features). Furthermore, the ML approaches were compared with the estimation method currently adopted by the analyzed company. The computational experiments assumed two main validation procedures, a standard cross-validation and a Rolling Window scheme. Overall, competitive and quality results were obtained by an Automated ML (AutoML) algorithm fed with newly engineered features. Indeed, the proposed AutoML model produces a small error (from 5 to 7 min), while requiring a reasonable computational effort. Finally, an eXplainable Artificial Intelligence (XAI) approach was applied to a trained AutoML model, demonstrating the extraction of useful explanatory knowledge for this domain.
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References
Abadi, M., et al.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/, software available from tensorflow.org
Azevedo, J., et al.: Predicting yarn breaks in textile fabrics: A machine learning approach. In: Cristani, M., Toro, C., Zanni-Merk, C., Howlett, R.J., Jain, L.C. (eds.) Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 26th International Conference KES-2022, Verona, Italy and Virtual Event, 7–9 September 2022. Procedia Computer Science, vol. 207, pp. 2301–2310. Elsevier (2022). https://doi.org/10.1016/j.procs.2022.09.289
Benevento, E., Aloini, D., Squicciarini, N.: Towards a real-time prediction of waiting times in emergency departments: A comparative analysis of machine learning techniques. Int. J. Forecast. 39(1), 192–208 (2023). https://doi.org/10.1016/j.ijforecast.2021.10.006
Caetano, N., Cortez, P., Laureano, R.M.S.: Using data mining for prediction of hospital length of stay: An application of the CRISP-DM methodology. In: Cordeiro, J., Hammoudi, S., Maciaszek, L.A., Camp, O., Filipe, J. (eds.) Enterprise Information Systems - 16th International Conference, ICEIS 2014, Lisbon, Portugal, April 27–30, 2014, Revised Selected Papers. LNBIP, vol. 227, pp. 149–166. Springer (2014). https://doi.org/10.1007/978-3-319-22348-3_9
Core, T.: Overfit and Underfit. https://www.tensorflow.org/tutorials/keras/overfit_and_underfit. (Accessed 28 Mar 2023)
Ferreira, L., Pilastri, A.L., Martins, C.M., Pires, P.M., Cortez, P.: A comparison of automl tools for machine learning, deep learning and xgboost. In: International Joint Conference on Neural Networks, IJCNN 2021, Shenzhen, China, 18–22 July 2021, pp. 1–8. IEEE (2021). https://doi.org/10.1109/IJCNN52387.2021.9534091
Gonçalves, F., Pereira, R., Ferreira, J., Vasconcelos, J.B., Melo, F., Velez, I.: Predictive Analysis in Healthcare: Emergency Wait Time Prediction. In: Novais, P., et al. (eds.) ISAmI2018 2018. AISC, vol. 806, pp. 138–145. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-01746-0_16
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. SSS, Springer, New York (2009). https://doi.org/10.1007/978-0-387-84858-7
Hollander, M., Wolfe, D.A., Chicken, E.: Nonparametric statistical methods. John Wiley & Sons, NJ, USA (2013)
Kuo, Y., et al.: An integrated approach of machine learning and systems thinking for waiting time prediction in an emergency department. Int. J. Med. Inform. 139, 104143 (2020). https://doi.org/10.1016/j.ijmedinf.2020.104143
Kyritsis, A.I., Deriaz, M.: A machine learning approach to waiting time prediction in queueing scenarios. In: Second International Conference on Artificial Intelligence for Industries, AI4I 2019, Laguna Hills, CA, USA, 25–27 September 2019. pp. 17–21. IEEE (2019). https://doi.org/10.1109/AI4I46381.2019.00013
LeDell, E., Poirier, S.: H2O AutoML: Scalable automatic machine learning. In: 7th ICML Workshop on Automated Machine Learning (AutoML) (July 2020). https://www.automl.org/wp-content/uploads/2020/07/AutoML_2020_paper_61.pdf
Lundberg, S.M., Lee, S.: A unified approach to interpreting model predictions. In: Guyon, I., von Luxburg, U., Bengio, S., Wallach, H.M., Fergus, R., Vishwanathan, S.V.N., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4–9 December 2017, Long Beach, CA, USA, pp. 4765–4774 (2017)
Matos, L.M., Cortez, P., Mendes, R., Moreau, A.: Using deep learning for mobile marketing user conversion prediction. In: International Joint Conference on Neural Networks, IJCNN 2019 Budapest, Hungary, 14–19 July 2019, pp. 1–8. IEEE (2019). https://doi.org/10.1109/IJCNN.2019.8851888
Pereira, P.J., Gonçalves, C., Nunes, L.L., Cortez, P., Pilastri, A.: AI4CITY - An Automated Machine Learning Platform for Smart Cities. In: SAC 2023: The 38th ACM/SIGAPP Symposium on Applied Computing, Tallinn, Estonia,27–31 March 2023, pp. 886–889. ACM (2023). https://doi.org/10.1145/3555776.3578740
Ribeiro, R., Pilastri, A.L., Moura, C., Rodrigues, F., Rocha, R., Cortez, P.: Predicting the tear strength of woven fabrics via automated machine learning: An application of the CRISP-DM methodology. In: Filipe, J., Smialek, M., Brodsky, A., Hammoudi, S. (eds.) Proceedings of the 22nd International Conference on Enterprise Information Systems, ICEIS 2020, Prague, Czech Republic, 5–7 May 2020, vol. 1, pp. 548–555. SCITEPRESS (2020). https://doi.org/10.5220/0009411205480555
Saaty, T.L.: Elements of queueing theory: with applications, vol. 34203. McGraw-Hill New York (1961)
Sanit-in, Y., Saikaew, K.R.: Prediction of waiting time in one stop service. Int. J. Mach. Learn. Comput. 9(3), 322–327 (2019)
Tashman, L.J.: Out-of-sample tests of forecasting accuracy: an analysis and review. Int. J. Forecast. 16(4), 437–450 (2000)
Wirth, R., Hipp, J.: Crisp-dm: Towards a standard process model for data mining. In: Proceedings of the 4th International Conference on the Practical Applications Of Knowledge Discovery And Data Mining, Manchester, vol. 1, pp. 29–39 (2000)
Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data mining: practical machine learning tools and techniques, 4th edn. Morgan Kaufmann (2016)
Acknowledgments
This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020 and the project “QOMPASS .: Solução de Gestão de Serviços de Atendimento multi-entidade, multi-serviço e multi-idioma” within the Project Scope NORTE-01-0247-FEDER-038462.
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Loureiro, C., Pereira, P.J., Cortez, P., Guimarães, P., Moreira, C., Pinho, A. (2023). Predicting Multiple Domain Queue Waiting Time via Machine Learning. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023. ICCSA 2023. Lecture Notes in Computer Science, vol 13956 . Springer, Cham. https://doi.org/10.1007/978-3-031-36805-9_27
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