Application of Feature Selection Based on Elastic Network and Random Forest in the Evaluation of Sports Effects

Abstract

With the rapid development of data mining and machine-learning technology and the outbreak of big sports data mining development challenges, sports data mining cannot simply use data statistical methods such as how to combine machine learning and data mining technology for effective mining and analysis of sports data, to provide useful advice for public physical exercise, and this is an urgent need to study. It is a kind of efficient sports data mining study through the feature selection algorithm. Around the difficult problems existing in the study of sports effect, given the limitations of existing data sets and traditional research methods, this paper starts from the data mining algorithm, builds the sports effect evaluation database, based on feature selection idea, using elastic network algorithm, random forest algorithm, and the influence of sports on the effect of physical indicators. The evaluation algorithm introduces machine learning algorithm and feature selection algorithm to guide the sports effect evaluation research. When studying the evaluation problem of sports effect, according to the constructed sports effect evaluation database, elastic network algorithm is added to regularize, optimize, and realize feature selection. When selecting the characteristics of different sports ability, using information gains indicators to rank the importance of characteristics, which can scientifically and accurately obtain the influence degree of sports on different physical indicators, make the physical fitness research more scientific, and can reveal the effect of sports as far as possible. Experimental results show that the selected features and ground-truth have good accuracy, good evaluation performance, and high accuracy compared with the baseline method.