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

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 industr...

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Veröffentlicht in:Wireless communications and mobile computing Jg. 2021; H. 1
Hauptverfasser: Gu, Zhu, He, Chaohu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Hindawi 2021
John Wiley & Sons, Inc
Schlagworte:
Accuracy
Algorithms
Business intelligence
Classification
Customer relationship management
Customers
Datasets
Decision analysis
Decision making
Decision trees
Learning
Methods
Mobile computing
Physical fitness
Secondary schools
Sports
Training
ISSN:1530-8669, 1530-8677
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1530-8669
1530-8677
DOI:10.1155/2021/4632722