Abstract
In this article, we propose identifying and analyzing the problem of relatively poor classification results related to a single decision class in sports data. First, we preprocess the data to obtain the decision class. Then, we implement a goal-oriented approach to the football data to improve the results for algorithms like ACDT (Ant Colony Decision Tree) and ACDF (Ant Colony Decision Forrest). The main difference in the case of the goal-oriented approach is the focus on particular classification measures like precision and recall. These measures are adapted to mentioned algorithms, and the whole approach is compared with the original algorithms based on the accuracy measure. Finally, numerical experiments are performed on the initially preprocessed real-world data set based on nine seasons of the German football Bundesliga.
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Głowania, S., Kozak, J., Juszczuk, P. (2023). Goal-Oriented Classification of Football Results. In: Nguyen, N.T., et al. Computational Collective Intelligence. ICCCI 2023. Lecture Notes in Computer Science(), vol 14162. Springer, Cham. https://doi.org/10.1007/978-3-031-41456-5_59
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