Retraction Notice: Application Research of Decision Tree Algorithm in Sports Grade Analysis

Abstract

This paper introduces and analyses the data mining in the management of students' sports grades. We use the decision tree in analysis of grades and investigate attribute selection measure including data cleaning. We take sports course score of some university for example and produce decision tree using ID3 algorithm which gives the detailed cal- culation process. Because the original algorithm lacks termination condition, we propose an improved algorithm which can help us to find the latency factor which impacts the sports grades. With the rapid development of higher education, sports grade analysis as an important guarantee for the scientific management constitutes the main part of the sports educa- tional assessment. The research on application of data min- ing in management of students' grades wants to talk how to get the useful uncovered information from the large amounts of data with the data mining and grade management (1-5). It introduces and analyses the data mining in the management of students' grades. It uses the decision tree in analysis of grades. It describes the function, status and deficiency of the management of students' grades. It tells us how to employ the decision tree in management of students' grades. It im- proves the ID3 arithmetic to analyze the students' grades so that we could find the latency factor which impacts the grades. If we find out the factors, we can offer the decision- making information to teachers. It also advances the quality of teaching (6-10). The sports grade analysis helps teachers to improve the teaching quality and provides decisions for school leaders. The decision tree-based classification model is widely used as its unique advantage. Firstly, the structure of the de- cision tree method is simple and it generates rules easy to understand. Secondly, the high efficiency of the decision tree model is more appropriate for the case of a large amount of data in the training set. Furthermore the computation of the decision tree algorithm is relatively not large. The decision tree method usually does not require knowledge of the train- ing data, and specializes in the treatment of non-numeric data. Finally, the decision tree method has high classification accuracy, and it is to identify common characteristics of li- brary objects, and classify them in accordance with the clas- sification model.