基于信息增益和随机森林算法的排球运动训练效果评估模型研究  

Research on the Evaluation Model of Volleyball Training Effectiveness Based on Information Gain and Random Forest Algorithm

作　　者：陈曦 CHEN Xi(Department of Physical Education,Chuzhou City Vocation College,Chuzhou 239000,Anhui,China)

机构地区：[1]滁州城市职业学院体育教学部,安徽滁州239000

出　　处：《喀什大学学报》2024年第6期79-84,共6页Journal of Kashi University

摘　　要：为了对排球运动效果进行有效评估,提出一种智能化的评价方法.首先采用随机森林算法进行运动特征的提取,通过构建决策树与信息增益实现运动特征的识别.同时考虑到随机森林算法面临的参数化问题,引入粒子群算法进行模型参数优化.在不同模型训练效果比较中,所提出的模型在召回率比较中表现最好,召回率均值为0.978,优于别的模型.在排球运动效果比较中,所提出的模型对8个主要指标的评估均高于0.935.此外,在误差以及耗时比较上,所提出模型均表现最好;所提出的智能评估技术可为排球等体育运动效果的评估以及老年群体健康的管理提供技术参考.In order to effectively evaluate the effectiveness of volleyball,an intelligent evaluation method is proposed.Firstly,the random forest algorithm is used to extract motion features,and the recognition of motion features is achieved by constructing a decision tree and information gain.Considering the parameterization problem faced by the random forest algorithm,the particle swarm optimization algorithm is introduced for model parameter optimization.In the comparison of training effects among different models,the proposed model performs best in the comparison of recall rates,with an average recall rate of 0.978,which is superior to other models.In the comparison of volleyball sports effects,the proposed model evaluated all 8 main indicators above 0.935.In addition,the proposed model performs best in terms of error and time comparison.The proposed intelligent evaluation technology provides technical reference for evaluating the effectiveness of sports such as volleyball and managing the health of the elderly population.

关 键 词：排球运动训练 效果评估 模型构建 信息增益 随机森林算法 

分 类 号：G808.1[文化科学—体育训练] TP181[文化科学—运动人体科学]