Research on decision support system of sports assistant teaching and training based on association rules and support vector machine

IF 1.5 Q2 COMPUTER SCIENCE, THEORY & METHODS
Tong-Zhigang
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引用次数: 3

Abstract

In the decision-making system of sports assistant teaching and training, the performance of such system is not robust to different situations with a low accuracy. To solve the problems in the decision-making system, we proposed a decision-making method combining association rules and support vector machine (SVM) in this paper. First of all, we give a computer-aided decision support system for sports assistant learning and teaching training, which is fully elaborated from three aspects: virtual reality (VR) technology, VR based sports assistant learning and teaching and situational cognition, and VR based sports assistant learning and teaching mode. After that, the paper gives the feature extraction of sports auxiliary teaching training through association rules and the decision-making of the extracted association rules by SVM. We have done two different experiments for both association rules mining and SVM on both experiment group and control group of databases. Experimental results have shown that the training characteristics of sports auxiliary teaching very well. In the decision support of association rules, compared with the existing BP neural network, linear discriminant analysis and naive Bayes and other methods, the SVM method has better effect of action recognition in decision support system of sports assistant teaching and training. The robustness is the best for the application of SVM. We provide a new perspective for the decision support of sports auxiliary teaching training by using association rules and SVM. Through the method of this paper, we can obtain better decision-making effect and more robust process of sports auxiliary teaching and training.
基于关联规则和支持向量机的体育辅助教学训练决策支持系统研究
在体育辅助教学与训练决策系统中,该系统对不同情况的鲁棒性不强,准确率较低。为了解决决策系统中存在的问题,本文提出了一种将关联规则与支持向量机(SVM)相结合的决策方法。首先,从虚拟现实(VR)技术、基于VR的体育辅助学习与教学与情境认知、基于VR的体育辅助学习与教学模式三个方面对计算机辅助决策支持系统进行了全面阐述。然后通过关联规则对体育辅助教学训练进行特征提取,并利用支持向量机对提取的关联规则进行决策。我们分别在实验组和对照组的数据库上对关联规则挖掘和支持向量机进行了两个不同的实验。实验结果表明,体育辅助教学具有很好的训练特点。在关联规则的决策支持中,与现有的BP神经网络、线性判别分析和朴素贝叶斯等方法相比,SVM方法在体育辅助教学训练决策支持系统中具有更好的动作识别效果。对于支持向量机的应用,鲁棒性是最好的。将关联规则与支持向量机相结合,为体育辅助教学训练的决策支持提供了新的视角。通过本文的方法,可以获得更好的决策效果和更稳健的体育辅助教学训练过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.80
自引率
23.10%
发文量
31
期刊介绍: The International Journal of Fuzzy Logic and Intelligent Systems (pISSN 1598-2645, eISSN 2093-744X) is published quarterly by the Korean Institute of Intelligent Systems. The official title of the journal is International Journal of Fuzzy Logic and Intelligent Systems and the abbreviated title is Int. J. Fuzzy Log. Intell. Syst. Some, or all, of the articles in the journal are indexed in SCOPUS, Korea Citation Index (KCI), DOI/CrossrRef, DBLP, and Google Scholar. The journal was launched in 2001 and dedicated to the dissemination of well-defined theoretical and empirical studies results that have a potential impact on the realization of intelligent systems based on fuzzy logic and intelligent systems theory. Specific topics include, but are not limited to: a) computational intelligence techniques including fuzzy logic systems, neural networks and evolutionary computation; b) intelligent control, instrumentation and robotics; c) adaptive signal and multimedia processing; d) intelligent information processing including pattern recognition and information processing; e) machine learning and smart systems including data mining and intelligent service practices; f) fuzzy theory and its applications.
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