Research on abnormal identification of physical fitness data in sports training based on ID3 decision tree algorithm

Yongming Chen

doi:10.1117/12.3031561

19 July 2024 Research on abnormal identification of physical fitness data in sports training based on ID3 decision tree algorithm

Yongming Chen

Author Affiliations +

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131815E (2024) https://doi.org/10.1117/12.3031561
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

Sports training physical fitness data has the characteristics of large scale and complex structure, which leads to low recognition efficiency of traditional data anomaly recognition methods. Therefore, a sports training physical fitness data anomaly recognition method based on ID3 decision tree algorithm is proposed. Mining sports training physical fitness data based on ID3 decision tree algorithm, preprocessing the mining data through cleaning, reconstruction, etc., calculating the outlier score value of each data, obtaining data outliers, and achieving abnormal recognition of sports training physical fitness data. The experimental results show that the recognition time of this method is shorter than that of the control group, and the recognition efficiency is higher.

Citation Download Citation

Yongming Chen "Research on abnormal identification of physical fitness data in sports training based on ID3 decision tree algorithm", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131815E (19 July 2024); https://doi.org/10.1117/12.3031561

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