Analyzing basketball games by a support vector machines with decision tree model

Support vector machines (SVMs) are an emerging and powerful technique in coping with classification problems. However, a lack of rule generation is a weakness of the SVM model, especially in analyzing sporting results. This investigation developed a hybrid model integrating the SVM technique and a d...

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Bibliographic Details
Published in:Neural computing & applications 2017-12, Vol.28 (12), p.4159-4167
Main Authors: Pai, Ping-Feng, ChangLiao, Lan-Hung, Lin, Kuo-Ping
Format: Article
Language:English
Subjects:
Artificial Intelligence
Basketball
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Decision analysis
Decision trees
Empirical analysis
Game theory
Image Processing and Computer Vision
Mathematical models
Original Article
Predictions
Probability and Statistics in Computer Science
Support vector machines
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Summary:Support vector machines (SVMs) are an emerging and powerful technique in coping with classification problems. However, a lack of rule generation is a weakness of the SVM model, especially in analyzing sporting results. This investigation developed a hybrid model integrating the SVM technique and a decision tree approach (HSVMDT) to predict the results of basketball games, and to provide rules to aid coaches in developing strategies. The HSVMDT model employed the unique strength of SVM and decision tree in generating rules and predicting the outcomes of games. With predicted outcomes of games, and rules yielded from the HSVMDT model, coaches can easily and quickly learn essential factors increasing the chances to win games. Empirical results showed that the proposed HSVMDT model can obtain relatively satisfactory prediction accuracy and therefore is a promising alternative for analyzing the results of basketball competitions.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-016-2321-9