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作 者:孙志远 陈爱国[1,2] 熊轩 朱昊[1] 史一凡 SUN Zhiyuan;CHEN Aiguo;XIONG Xuan;ZHU Hao;SHI Yifan(College of Physical Education,Yangzhou University,Yangzhou 225127,China;Institute of Sports Exercise and Brain Science,Yangzhou University,Yangzhou225127,China)
机构地区:[1]扬州大学体育学院,江苏扬州225127 [2]扬州大学体育运动与脑科学研究所,江苏扬州225127
出 处:《南京体育学院学报》2021年第7期40-45,共6页Journal of Nanjing Sports Institute
基 金:国家自然科学基金资助项目:有氧运动促进儿童执行功能发展的脑网络机制研究(31771243)。
摘 要:近年来,纵向实验在体育科学研究中越来越多,如何可靠地处理在研究中出现的后测数据缺失,成为一个摆在研究者面前需要解决的难题。首先,指出已有缺失值处理方法存在的不足;其次,探讨了基于机器学习的预测模型在缺失值处理中的优点;最后,以运动对大学生执行控制影响的纵向研究为例,选择基线有氧适能和基线执行控制为模型特征,以机器学习中的支持向量计算法建立后测执行控制缺失值的预测模型,详细呈现了应用机器学习处理后测数据缺失值的步骤和结果可靠性。表明基于机器学习的纵向缺失值处理方法可以为体育领域纵向研究中后测数据缺失值的处理提供可靠科学手段。In recent years,more and more longitudinal experiments had been carried out in sports science research,how to deal with the missing values of post-test data in the research reliably had became a difficult problem for researchers to solve.Firstly,the shortcomings of the existing missing value processing methods were pointed out,and then the advantages of the predictive model based on machine learning in missing value processing were discussed.Finally,taking the longitudinal research on the influence of exercise on executive control of college students as an example,baseline aerobic fitness and baseline executive control were selected as the model features,and establishment of prediction model of the missing values on executive control at post-test used support vector machine algorithm,and the steps and reliability of the results are presented in detail.This study showed that the longitudinal missing value processing method based on machine learning could provide a scientific and reliable means to deal with the missing value of post-test data in longitudinal research of sports field.
分 类 号:G804.2[文化科学—运动人体科学]
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