Title :
Data mining approaches to the characterizations of nominees for FIFA Ballon d´Or award
Author :
Shibata, Renato Toshiaki ; Inuiguchi, Masahiro
Author_Institution :
Dept. of Syst. Sci., Osaka Univ., Toyonaka, Japan
Abstract :
In this paper, data mining methods are applied to a data set of recent football players in order to characterize the players who are nominated for FIFA Ballon d´Or award. We assume there is an impartial criterion for the nomination considering only player´s in-game statistics. Under this assumption, we applied four data mining algorithms, possessing distinctive features each other: OneR, C4.5, MLEM2 and DOMLEM. The results obtained by each method are discussed and compared among them in order to evaluate how accurate they are according to a football expert´s opinion.
Keywords :
data mining; expert systems; sport; statistical analysis; C4.5; DOMLEM; FIFA Ballon d´Or award; MLEM2; OneR; data mining algorithm; data mining approach; football expert opinion; football player; in-game statistics; nomination; Accuracy; Awards activities; Classification algorithms; Data mining; Decision trees; Games; Software; ballon d´or; data mining; footballl; rule induction;
Conference_Titel :
Soft Computing and Intelligent Systems (SCIS), 2014 Joint 7th International Conference on and Advanced Intelligent Systems (ISIS), 15th International Symposium on
DOI :
10.1109/SCIS-ISIS.2014.7044894
