6 144 CHAPTER 6 REFERENCES [1] H. He and E. A. Garcia, “Learning from imbalanced data,” IEEE Trans. Knowl. Data Eng., vol. 21, no. 9, pp. 1263–1284, 2009, doi: 10.1007/978-3-030-04663-7_4. [2] S. Kotsiantis, D. Kanellopoulos, and P. Pintelas, “Handling imbalanced datasets : A review,” Science (80-. )., vol. 30, no. 1, pp. 25–36, 2006. [3] M. Buchheit and B. M. Simpson, “Player-Tracking Technology : Half-Full or Half-Empty Glass ?,” Int. J. Sports Physiol. Perform., vol. 12, no. S2, pp. 35–41, 2017. [4] S. Uddin, A. Khan, M. E. Hossain, and M. A. Moni, “Comparing different supervised machine learning algorithms for disease prediction,” BMC Med. Inform. Decis. Mak., vol. 19, no. 1, pp. 1–16, 2019, doi: 10.1186/s12911-019-1004-8. [5] I. Ibrahim and A. Abdulazeez, “The Role of Machine Learning Algorithms for Diagnosing Diseases,” J. Appl. Sci. Technol. Trends, vol. 2, no. 01, pp. 10–19, 2021, doi: 10.38094/jastt20179. [6] M. J. van der Laan, E. C. Polley, and A. E. Hubbard, “Super Learner,” Stat. Appl. Genet. Mol. Biol., vol. 6, no. 1, 2007. [7] S. Makinen, H. Skogstrom, E. Laaksonen, and T. Mikkonen, “Who needs MLOps: What data scientists seek to accomplish and how can MLOps help?,” Proc. - 2021 IEEE/ACM 1st Work. AI Eng. - Softw. Eng. AI, WAIN 2021, pp. 109–112, 2021, doi: 10.1109/WAIN52551.2021.00024. [8] J. Pearl and D. Mackenzie, The Book of Why. New York: Basic Books, 2018. [9] C. Lago, L. Casais, E. Dominguez, and J. Sampaio, “The effects of situational variables on distance covered at various speeds in elite soccer,” Eur. J. Sport Sci., vol. 10, no. 2, pp. 103–109, 2010, doi: 10.1080/17461390903273994. [10] J. Castellano, A. Blanco-Villaseñor, and D. Álvarez, "Contextual variables and time-motion analysis in soccer," Int. J. Sports Med., vol. 32, no. 6, pp. 415–421, 2011, doi: 10.1055/s-0031-1271771. [11] V. I. Kalapotharakos, A. Gkaros, and E. Vassliades, “Influence of contextual factors on match running performance in elite soccer team,” J. Phys. Educ. Sport, vol. 20, no. 6, pp. 3267–3272, 2020, doi: 10.7752/ jpes.2020.s6443. [12] U. Fayyad, G. Piatetsky-shapiro, and P. Smyth, “From Data Mining to Knowledge Discovery in Databases,” AI Mag., vol. 17, no. 3, pp. 37–54, 1996, doi: 10.1007/978-3-319-18032-8_50. [13] A. Sims et al., “Data-Centric Automated Data Mining,” Big Data Res., vol. 2, no. 2, pp. 1–36, Jun. 2016, doi: 10.1519/JSC.0000000000000499. [14] C. A. Palacios, J. A. Reyes-Suárez, L. A. Bearzotti, V. Leiva, and C. Marchant, "Knowledge Discovery for Higher Education Student Retention Based on Data Mining: Machine Learning Algorithms and Case Study in Chile," 2021, doi: 10.3390/e23040485. [15] M. J. van der Laan and S. Rose, Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies. 2018. [16] J. G. Claudino, D.-O. Capanema, T.-V. De-Souza, J. C. Serrão, A.-C. Machado Pereira, and G.-P. Nassis, "Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review," Sport. Med. - Open, vol. 5, no. 1, 2019. [17] E. Morgulev, O. H. Azar, and R. Lidor, “Sports analytics and the big-data era,” Int. J. Data Sci. Anal., vol. 5,
RkJQdWJsaXNoZXIy MjY0ODMw