Characterisation of Goal Scoring Patterns during Open Play Related to Zone Pitch Division and Number of Players Involved in the 2018 FIFA World Cup

The aim of this study was to characterise all the goal scoring patterns during open play (elaborate attacks versus counterattacks) related to zone pitch division and the number of players involved in the 2018 FIFA World Cup in Russia. An Iterative Dichotomiser 3 (ID3) decision tree algorithm was used to classify all the goal scoring patterns (94 goals in 64 matches). The results did not show statistical differences between the type of scoring goal during the 2018 FIFA World Cup (p > 0.05; ES = Moderate). According to the result of the patterns of how the goals were achieved, an ID3 algorithm decision tree with seven classification decision nodes was calculated. Consequently, this study may aid national team coaches for the next World Cup to establish notational analyses and spatial-temporal relations to understand how scoring patterns during open play are related to zone pitch division and the number of players involved.

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Application of Decision Tree Algorithm in Lumber Hierarchies

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.466-467.308 ◽

2012 ◽

Vol 466-467 ◽

pp. 308-313

Author(s):

Dan Guo

Keyword(s):

Data Mining ◽

Decision Tree ◽

Inductive Reasoning ◽

Decision Tree Algorithm ◽

Discrete Function ◽

Tree Algorithm ◽

Id3 Algorithm ◽

Noise Data ◽

Construction Model

The decision tree algorithm is a kind of approximate discrete function value method with high precision, construction model of classification of noise data is simple and has good robustness etc, it is currently the most widely used in one of the inductive reasoning algorithms in data mining, extensive attention by researchers. This paper selects the decision tree ID3 algorithm to realize the standardization of lumber level division, to ensure the accuracy of the lumber division, while improving the partition of speed.

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An Improved ID3 Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.444-445.723 ◽

2013 ◽

Vol 444-445 ◽

pp. 723-727 ◽

Cited By ~ 3

Author(s):

Ju Fang Li ◽

Jin Hui Lei ◽

Xiao Xia Zhao ◽

Chang Chang Zhang ◽

Xue Xue Han

Keyword(s):

Decision Tree ◽

Predictive Accuracy ◽

Experimental Results ◽

Decision Tree Algorithm ◽

Tree Algorithm ◽

Id3 Algorithm ◽

Improved Algorithm

ID3 algorithm is the earliest and most influential of decision tree algorithm. This paper discusses the basic idea and implementation methods of the ID3 algorithm. A new algorithm based on attribute similarity for multivalued bias of ID3 algorithm was proposed, and the experimental results proved that the improved algorithm has strong predictive accuracy and better understandability.

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

Wireless Communications and Mobile Computing ◽

10.1155/2021/4632722 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Zhu Gu ◽

Chaohu He

Keyword(s):

Decision Tree ◽

Mobile Computing ◽

Classification Accuracy ◽

Decision Tree Algorithm ◽

Fuzzy Decision ◽

Fuzzy Decision Tree ◽

Data Set ◽

Tree Algorithm ◽

Id3 Algorithm ◽

Fitness Industry

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.

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An Improved ID3 Decision Tree Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.962-965.2842 ◽

2014 ◽

Vol 962-965 ◽

pp. 2842-2847 ◽

Cited By ~ 3

Author(s):

Xiao Juan Chen ◽

Zhi Gang Zhang ◽

Yue Tong

Keyword(s):

Decision Tree ◽

Information Entropy ◽

Information Gain ◽

Classification Algorithm ◽

Learning Ability ◽

Decision Tree Algorithm ◽

Tree Algorithm ◽

Decision Tree Classification ◽

Id3 Algorithm ◽

Improved Algorithm

As the classical algorithm of the decision tree classification algorithm, ID3 algorithm is famous for the merits of high classifying speed, strong learning ability and easy construction. But when used to make classification, the problem of inclining to choose attributions which have many values affect its practicality. This paper presents an improved algorithm based on the expectation information entropy and Association Function instead of the traditional information gain. In the improved algorithm, it modified the expectation information entropy with the improved Association Function and the number of the attributes values. The experiment result shows that the improved algorithm can get more reasonable and more effective rules.

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The Application of Decision Tree Algorithm in the Employment Management System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.543-547.1639 ◽

2014 ◽

Vol 543-547 ◽

pp. 1639-1642 ◽

Cited By ~ 2

Author(s):

Liang Li ◽

Ying Zheng ◽

Xiao Hua Sun ◽

Fu Shun Wang

Keyword(s):

Data Mining ◽

Decision Tree ◽

Effective Means ◽

University Graduates ◽

Decision Tree Algorithm ◽

Tree Algorithm ◽

Id3 Algorithm ◽

Decision Attributes ◽

C4.5 Algorithm ◽

Employment Data

According to students' employment problem, employment data mining model of university graduates is presented. The decision tree is very effective means for classification, which is proposed according to the characteristics of employment data and C4.5 algorithm. The C4.5 algorithm is improved from ID3 algorithm that is the core algorithm in the decision tree. The C4.5 algorithm is suitable for its simple construction, high processing speed and easy implementation. The model includes preprocess of the data of employment selection of decision attributes, implementation of mining algorithm, and obtainment of rules from the decision tree. The rules point out which decision attributes decide the classification of employers. Case study shows that the decision tree algorithm applied to employment information data mining, can classify data of employment correctly with simple structure and faster speed, and find some valuable results for analysis and decision. so the proposed algorithm in this paper is effective.

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