References
- K. B. Goo & S. B. Kim. (2018). Issues of Sports Circle in the Era of 4th Industrial Revolution. The Korean Society for the Philosophy of Sport, Dance & Martial Arts, 26(2), 7-17.
- W. J. Kim, Y. S. Choi & D. H. Yoo. (2018). Development of Win-Loss Prediction Models and Strategies for Improving Winning Rate of the Korean Professional Baseball Teams Using Data Mining Techniques. Korean Journal of Sport Management, 23(3), 88-104. DOI : 10.31308/KSSM.23.3.6
- J. H. Cho. (2012). Utilization and Prospect of Sport Big Data. The Korean Journal of Measurement and Evaluation in Physical Education and Sports Science, 14(3), 1-11. DOI : 10.21797/ksme.2012.14.3.001
- J. W. Lim & H. J. Kim. (2008). Setting Sprint Zone for performance analysis in Field-Hockey: Using Global Positioning System (GPS). The Korean Journal of Measurement and Evaluation in Physical Education and Sports Science, 10(1), 69-79. DOI : 10.21797/ksme.2008.10.1.006
- I. S. Yeo & C. H. Park. (1998). Ethics of competition and victory in sport: The Korean Society for the Philosophy of Sport, Dance & Martial Arts, 6(1), 67-88. UCI(KEPA) : I410-ECN-0101-2018-069-001973573
- D. Jennings, S. Cormack, A. J. Coutts, L. Boyd & R. J. Aughey. (2010). The validity and reliability of GPS units for measuring distance in team sport specific running patterns. International journal of sports physiology and performance, 5(3), 328-341. DOI:10.1123/ijspp.5.3.328
- D. W. Wundersitz, P. B. Gastin, S. Robertson, P. C. Davey & K. J. Netto. (2015). Validation of a trunk-mounted accelerometer to measure peak impacts during team sport movements. International journal of sports medicine, 36(09), 742-746. DOI: 10.1055/s-0035-1547265
- S. K. Min. (2014). Sports Science: On-site support using the Global Positioning System (GPS) in field hockey competitions compared to the Asian Games. Sports science, 128, 48-53.
- Y. H. Jung, S. H. Eo, H. S. Moon & H. J. Cho. (2010). A Study for Improving the Performance of Data Mining Using Ensemble Techniques. Communications for Statistical Applications and Methods, 17(4), 561-S74. UCI : G704-000420.2010.17.4.006 https://doi.org/10.5351/CKSS.2010.17.4.561
- W. H. Lee. (2006). development of soccer ranking prediction model using neural network analysis. Unpublished doctoral dissertation, Myongji University, Seoul.
- Y. H. Oh, H. Kim, J. S. Yun & J. S. Lee. (2014). Using Data Mining Techniques to Predict Win-Loss in Korean Professional Baseball Games. Journal of the Korean Institute of Industrial Engineers, 40(1), 8-17. DOI : 10.7232/JKIIE.2014.40.1.008
- Y. C. Kim. (2018). E-sports game prediction using machine learning : focusing on League of Legends. Unpublished master's thesis, Chung-Ang University, Seoul.
- D. Miljkovic, L. Gajic, A. Kovacevic & Z. Konjovic. (2010, September). The use of data mining for basketball matches outcomes prediction. In IEEE 8th International Symposium on Intelligent Systems and Informatics. (pp. 309-312). IEEE. DOI: 10.1109/SISY.2010.5647440
- N. Razali, A. Mustapha, F. A. Yatim & R. Ab Aziz. (2017, August). Predicting football matches results using Bayesian networks for English Premier League (EPL). In Iop conference series: Materials science and engineering, 226(1), p.012099. DOI: 10.1088/1757-899X/226/1/012099
- J. H. Yi & S. W. Lee. (2020). Prediction of English Premier League Game Using an Ensemble Technique. KIPS Transactions on Software and Data Engineering, 9(5), 161-168. https://doi.org/10.3745/KTSDE.2020.9.5.161
- W. Cai, D. Yu, Z. Wu, X. Du & T. Zhou. (2019). A hybrid ensemble learning framework for basketball outcomes prediction. Physical A: Statistical Mechanics and its Applications, 528, 121461. DOI: 10.1016/j.physa.2019.121461
- A. Groll, C. Ley, G. Schauberger & H. Van Eetvelde. (2019). A hybrid random forest to predict soccer matches in international tournaments. Journal of Quantitative Analysis in Sports, 15(4), 271-287. DOI: 10.1515/jqas-2018-0060
- J. C. Park, K. S. Yoon & J. E. Kim. (2015). Movement Analysis of Women's Handball Players by Position using Inertial Measurement Units. Journal of the Korea Convergence Society, 11(4), 343-350. DOI : 10.15207/JKCS.2020.11.4.343
- D. R. Jang & M. J. Park. (2020). A Study on the Art Price Prediction Model Using the Random Forests. Journal of Applied Reliability, 20(1), 34-42. DOI : 10.33162/JAR.2020.3.20.1.34
- S. J. Kim & H. C. Ahn. (2016). Application of Random Forests to Corporate Credit Rating Prediction. Kyungsung University Institute for Industrial Development, 32(1), 187-211. DOI : 10.22793/indinn.2016.32.1.006
- Y. S. Lee, H. J. Oh & M. K. Kim. (2005). An Empirical Comparison of Bagging, Boosting and Support Vector Machine Classifiers in Data Mining. The Korean Journal of applied Statistics, 18(2), 343-354. UCI : G704-000408.2005.18.2.003 https://doi.org/10.5351/KJAS.2005.18.2.343
- J. C. W. Chan & D. Paelinckx. (2008). Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sensing of Environment, 112(6), 2999-3011. DOI: 10.1016/j.rse.2008.02.011
- H. H. Lee & J. E. Kim. (2019). Application of Electronic Performance Tracking System (EPTS) for Dance Evaluation of Dancers and Prediction Model of Injury Prevention. The Korean Journal of Sport, 17(4), 1597-1607.
- S. J. Kang. (2019). Preliminary Study on the Prediction of Human Kinematic Motion Analysis Using Artificial Intelligence. Korean Society for Precision Engineering, 19-19. DOI:10.1007/978-981-13-9129-3_1
- H. Y. Jung, Y. J. Na, C. W. Wang & S. D. Min. (2014). Multi-objective system using EMG and accelerometer. Journal of Electrical Engineering & Technology. Summer Academic Conference, 1,502 - 1,503. UCI(KEPA) : I410-ECN-0101-2016-560-001853938