[Retracted] Application of Fuzzy Decision Tree Algorithm Based on Mobile Computing in Sports Fitness Member Management

Abstract

After the reform and the opening, the economy of our country has developed rapidly, and the living conditions of the people have become better and better. As a result, they have a lot of time to pay attention to their health, which has promoted the rapid development of the sports and fitness industry in my country. In response to the increasing development of the sports and fitness sector of my country, the current state of the administration of members of the sports fitness industry does not keep pace with the development of the sports and fitness industry of my country. Based on this, this article uses a fuzzy decision tree algorithm to establish a decision tree based on the characteristics of customer data and loses existing customers. Analyzing the situation is of strategic significance for improving the competitiveness of the club. This article selects the 7 most commonly used data sets from the UCI data set as the initial experimental data for model training in three different formats and then uses the data of a specific club member to conduct experiments, using these data files as training samples to construct a vague analysis of the decision tree to overturn the customer to analyze the main factors of customer change. Experiments show that the fuzzy decision tree ID3 algorithm based on mobile computing has the highest accuracy in the Iris data set, reaching 97.8%, and the accuracy rate in the Wine data set is the smallest, only 65.2%. The mobile computing-based fuzzy decision tree ID3 algorithm proposed in this paper obtained the highest correct rate (86.32%). This shows that, compared to traditional analysis methods, the blurred decision tree obtained for churn client analysis has the advantages of high classification accuracy and is understandable so that ideal classification accuracy can be achieved when the tree is small.

Identification Method of Influencing Factors of Hospital Catering Service Satisfaction Based on Decision Tree Algorithm

“…Based on this, Gu Z uses the fuzzy decision tree algorithm to build a decision tree according to the characteristics of customer data and the loss of existing customers. Analysis of the situation is of strategic significance to improve the competitiveness of clubs [ 10 ]. Decision trees are one of the most popular machine learning algorithms that repeatedly divide data into groups or classes.…”

Application of Decision Tree in PE Teaching Analysis and Management under the Background of Big Data

“…Curran and Standage [20] put forward new ideas, as well as the overall instructional reform and experiment under its guidance, including PE teaching objectives, teaching contents, teaching methods, organizational forms, and teaching means, and gradually established one group or even several groups, in line with the spirit of reform and the teaching reality of colleges and universities in China's teaching procedures with strong operability and systematization, corresponding structure and function of PE teaching curriculum and teaching process structure, and framework or type [20]. Gu and He [21] put forward that there are still many problems in the current PE teaching in higher vocational colleges, and the teaching ideas cannot keep up with the development requirements of the times. When carrying out PE teaching, they are greatly influenced by the traditional teaching ideas, and the teaching methods are mainly classroom teaching, focusing on the teaching of knowledge points in textbooks, but there is a lack of teaching on physical skills [21].…”

Comprehensive Evaluation of College Students’ Physical Health and Sports Mode Recommendation Model Based on Decision Tree Classification Model

“…In order to accelerate the development of the sports and fitness industry, Gu and He used ID3 fuzzy decision tree to build a tree according to the characteristics of customer data and the loss of customers, and analyzed the main factors of customer changes. Finally, it was concluded that the accuracy of ID3 fuzzy decision tree was as high as 97.8%, which could accurately analyze the causes of customer loss [13]. Li et al have designed a system based on ID3 and C4 5.…”

“…We sum each row of the normalized matrix and then calculate the eigenvector of the matrix, which is the weight vector, as shown in equation (13). In equation ( 13), the sum value of each row is as follows:…”