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Research and performance analysis of random forest-based feature selection algorithm in sports effectiveness

Yujiao Li1, Yingjie Mu2

  • 1Harbin Normal University, Harbin, 150025, China.

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This study introduces a novel sports big data mining method combining Random Forest and Artificial Raindrop algorithms. The approach enhances classification accuracy for analyzing exercise impacts on human physiology, significantly benefiting sports education.

Keywords:
Data statisticsFeature selection algorithmRandom forestSports big dataSports data

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Area of Science:

  • Sports Science
  • Data Mining
  • Machine Learning

Background:

  • The proliferation of sports big data presents challenges for traditional data mining techniques.
  • Existing methods struggle with low classification accuracy and insufficient refinement in targeted sports data analysis.
  • Feature extraction and construction are critical but often inadequate with basic statistical approaches.

Purpose of the Study:

  • To address limitations in traditional sports big data mining, particularly low accuracy.
  • To develop and evaluate a novel feature selection-based data mining method for sports big data.
  • To accurately assess the influence of exercise on human physiological indicators.

Main Methods:

  • Integration of the Random Forest algorithm with the Artificial Raindrop algorithm.
  • Application of a feature selection approach for sports big data analysis.
  • Utilizing the information gain index to rank feature importance and evaluate motion effect impacts.

Main Results:

  • The proposed algorithm demonstrated superior performance in accuracy and F1 scores on training and testing datasets.
  • Achieved accuracies of 0.849 ± 0.021 (training) and 0.819 ± 0.022 (testing).
  • Achieved F1 scores of 0.837 ± 0.020 (training) and 0.864 ± 0.021 (testing).

Conclusions:

  • The Random Forest-based feature selection algorithm significantly outperforms traditional methods in accuracy and performance.
  • The developed data analysis method enables accurate and efficient utilization of sports big data.
  • This approach holds substantial importance for advancing the sports education industry through data-driven insights.