Abstract
The paper delves into the realm of machine learning applications in sports, particularly focusing on the creation of ensembles of classifiers. It introduces a groundbreaking steps approach, utilizing Dynamic Classifier Selection (DCS), to elevate the precision of predicting outcomes in European football leagues. The methodology involves a meticulous exploration of the integration, preparation, and selection of diverse datasets, presenting a stark contrast to traditional classifier techniques. Rigorous experiments were conducted to validate the efficacy of the proposed steps approach, revealing a significant improvement in prediction accuracy compared to conventional methods.
The article not only establishes the effectiveness of the steps approach but also hints at promising avenues for future research. These include the exploration of various voting schemes, the automation of ensemble construction, and the investigation of adaptive voting schemes. The overarching goal is to refine and enhance the process of classifier selection in the analysis of sports data. The results of this research pave the way for an automatic approach to building ensembles of classifiers, addressing a notable gap in the existing literature where such methodologies are not explicitly outlined.
The primary focus of the research was to develop an automatic approach for creating classifier ensembles, aiming to substantially enhance the accuracy of sports data predictions. The absence of an explicit automatic approach in the current literature presented an opportunity for this study to contribute a novel step approach. The obtained results not only showcase the efficacy of the proposed method in predicting match outcomes accurately but also highlight the versatility of the approach by its applicability to real data from various sectors. This multifaceted contribution positions the steps approach as a valuable asset in the realm of sports data analysis and prediction.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996). https://doi.org/10.1007/BF00058655
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324
Brun, A.L., Britto, A.S., Jr., Oliveira, L.S., Enembreck, F., Sabourin, R.: A framework for dynamic classifier selection oriented by the classification problem difficulty. Pattern Recogn. 76, 175–190 (2018)
Didaci, L., Giacinto, G., Roli, F., Marcialis, G.L.: A study on the performances of dynamic classifier selection based on local accuracy estimation. Pattern Recogn. 38(11), 2188–2191 (2005)
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17(3), 37–37 (1996)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997). https://doi.org/10.1006/jcss.1997.1504
Freund, Y., Schapire, R.E., et al.: Experiments with a new boosting algorithm. In: ICML, vol. 96, pp. 148–156. Citeseer (1996)
Giacinto, G., Roli, F.: Dynamic classifier selection based on multiple classifier behaviour. Pattern Recogn. 34(9), 1879–1881 (2001)
Głowania, S., Kozak, J., Juszczuk, P.: New voting schemas for heterogeneous ensemble of classifiers in the problem of football results prediction. Procedia Comput. Sci. 207, 3393–3402 (2022)
Juszczuk, P., Kozak, J., Dziczkowski, G., Głowania, S., Jach, T., Probierz, B.: Real-world data difficulty estimation with the use of entropy. Entropy 23(12), 1621 (2021). https://doi.org/10.3390/e23121621
Kozak, J.: Decision Tree and Ensemble Learning Based on Ant Colony Optimization. Springer (2019). https://doi.org/10.1007/978-3-319-93752-6
Kozak, J., Głowania, S.: Heterogeneous ensembles of classifiers in predicting bundesliga football results. Procedia Comput. Sci. 192, 1573–1582 (2021). https://doi.org/10.1016/j.procs.2021.08.161
Kozak, J., Probierz, B., Kania, K., Juszczuk, P.: Preference-driven classification measure. Entropy 24(4), 531 (2022)
Leung, C.K., Joseph, K.W.: Sports data mining: predicting results for the college football games. Procedia Comput. Sci. 35, 710–719 (2014). https://doi.org/10.1016/j.procs.2014.08.153
Maszczyk, A., Gołaś, A., Pietraszewski, P., Roczniok, R., Zajac, A., Stanula, A.: Application of neural and regression models in sports results prediction. Procedia-Soc. Behav. Sci. 117, 482–487 (2014). https://doi.org/10.1016/j.sbspro.2014.02.249
McCabe, A., Trevathan, J.: Artificial intelligence in sports prediction. In: Fifth International Conference on Information Technology: New Generations (ITNG 2008), pp. 1194–1197. IEEE (2008). https://doi.org/10.1109/ITNG.2008.203
Pai, P.F., ChangLiao, L.H., Lin, K.P.: Analyzing basketball games by a support vector machines with decision tree model. Neural Comput. Appl. 28(12), 4159–4167 (2017). https://doi.org/10.1007/s00521-016-2321-9
Rokach, L.: Ensemble methods for classifiers. In: Data Mining and Knowledge Discovery Handbook, pp. 957–980. Springer (2005). https://doi.org/10.1007/0-387-25465-X_45
Schauberger, G., Groll, A., Tutz, G.: Modeling football results in the german bundesliga using match-specific covariates (2016). https://doi.org/10.5282/ubm/epub.29390
Sujatha, K., Godhavari, T., Bhavani, N.P.: Football match statistics prediction using artificial neural networks. Int. J. Math. Comput. Methods 3 (2018)
Tian, Y., Wang, X.: Svm ensemble method based on improved iteration process of Adaboost algorithm. In: 2017 29th Chinese Control and Decision Conference (CCDC), pp. 4026–4032. IEEE (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Głowania, S. (2024). Stepwise Approach to Automatically Building an Ensemble of Classifiers on Football Data. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2024. Communications in Computer and Information Science, vol 2145. Springer, Singapore. https://doi.org/10.1007/978-981-97-5934-7_21
Download citation
DOI: https://doi.org/10.1007/978-981-97-5934-7_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-5933-0
Online ISBN: 978-981-97-5934-7
eBook Packages: Computer ScienceComputer Science (R0)