{"title":"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","authors":"Tong-Zhigang","doi":"10.3233/JIFS-219035","DOIUrl":null,"url":null,"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.","PeriodicalId":44705,"journal":{"name":"International Journal of Fuzzy Logic and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Research on decision support system of sports assistant teaching and training based on association rules and support vector machine\",\"authors\":\"Tong-Zhigang\",\"doi\":\"10.3233/JIFS-219035\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":44705,\"journal\":{\"name\":\"International Journal of Fuzzy Logic and Intelligent Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Fuzzy Logic and Intelligent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/JIFS-219035\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Fuzzy Logic and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/JIFS-219035","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
Research on decision support system of sports assistant teaching and training based on association rules and support vector machine
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.
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.