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A method for classifying and mining online teaching data in universities based on decision tree algorithm

INTERNATIONAL JOURNAL OF CONTINUING ENGINEERING EDUCATION AND LIFE-LONG LEARNING 2025年第1-2期35卷 171-186页

作者： Wang, Fei Wu, Xiaoyan Henan Polytech Sch Continuing Educ Nanyang 473000 Henan Peoples R China Henan Polytech Coll Ind Sch Mech Engn Nanyang 473000 Henan Peoples R China

In response to the problems of low execution efficiency and high probability of multi-classification loss in the classification mining of online teaching data in universities, this paper proposes a decision tree algorithm based method for classification mining of online teaching data in universities. This method calculates multi-class losses through an unbiased risk estimator and minimises the difference between real and non-real labels for data pre-processing. Then, based on the complexity of experience, the degree of feature fitting is considered to determine the set of feature data, and redundant features are removed from the perspective of two-dimensional real space for feature extraction. Finally, use decision tree algorithm for classification mining. The experimental results show that this method improves execution efficiency and reduces the risk of data loss.

关键词： decision tree algorithm online teaching data in universities classification mining multi-classification loss function space

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Construction of decision tree algorithm in English-Based Virtual Database Platform

Construction of Decision Tree Algorithm in English-Based Vir...

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作者： Guo, Li Tian, Qi Anhui Xinhua University Anhui Hefei China

With the ever-increasing volume of data and the advent of the information age, virtual database platforms have become essential tools for managing and processing large amounts of data. In the construction of an English-based virtual database platform, the decision tree algorithm, as a widely-used machine learning algorithm, can effectively handle tasks such as data classification, prediction, and decision support. This paper aims to explore the application of the decision tree algorithm in the construction of an English-based virtual database platform and analyze its advantages and challenges. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data classification decision support decision tree algorithm English-based virtual database platform Prediction

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The data analysis of sports training by ID3 decision tree algorithm and deep learning

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Scientific Reports 2025年第1期15卷 1-11页

作者： Wang, Kaigong Wang, Lei Sun, Jiduo Sports institute or college Northeast Electric Power University Jilin 132012 China Sports institute or college Minzu University of China Beijing 100036 China

In order to improve the accuracy and efficiency of sports training data analysis, this paper proposes an optimized analysis model by combining Iterative Dichotomiser 3 (ID3) decision tree algorithm and deep learning model. As an important scientific tool, sports training data analysis aims to provide decision support for athletes and coaches, optimize training programs and improve sports performance through accurate data mining and model prediction. Traditional analysis methods have shortcomings in dealing with complex and multidimensional data, while analysis methods based on artificial intelligence can significantly improve the ability of feature extraction and prediction. Based on this background, this paper comprehensively evaluates the performance of each model in different dimensions by comparing six key indicators: mean square error (MSE), mean absolute error (MAE), information gain, feature importance, sports performance improvement rate and training target achievement rate. The experimental results show that the optimized model has the best MSE, and its MSE is only 1.05 under the information gain. It is significantly better than Extreme Gradient Boosting (XGBoost) of 1.48 and Capsule Networks (CapsNets) of 1.25. In terms of MAE, the minimum error of the optimized model is 0.65, while the maximum error of XGBoost is 1.11. In terms of information gain and feature importance, the optimization model is also outstanding, with the highest information gain of 1.02 and the feature importance maintained at a high level of 0.94 in many dimensions. Meanwhile, the optimized model is superior to other models in sports performance improvement rate (up to 6.71) and training target achievement rate (up to 78.32%). Therefore, this paper has certain reference significance to the field of sports training data analysis. © The Author(s) 2025.

关键词： decision tree algorithm Deep learning technology Information gain Sports training data analysis

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decision tree algorithm for detection of spatial processes in landscape transformation

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ENVIRONMENTAL MANAGEMENT 2004年第1期33卷 62-73页

作者： Bogaert, J Ceulemans, R Salvador-Van Eysenrode, D Free Univ Brussels Ecole Interfac Bioingn B-1050 Brussels Belgium Univ Instelling Antwerp Dept Biol Res Grp Plant & Vegetat Ecol B-2610 Antwerp Belgium

The conversion of landscapes by human activities results in widespread changes in landscape spatial structure. Regardless of the type of land conversion, there appears to be a limited number of common spatial configurations that result from such land transformation processes. Some of these configurations are considered optimal or more desirable than others. Based on pattern geometry, we define ten processes responsible for pattern change: aggregation, attrition, creation, deformation, dissection, enlargement, fragmentation, perforation, shift, and shrinkage. A novelty in this contribution is the inclusion of transformation processes causing expansion of the land cover of interest. Consequently, we propose a decision tree algorithm that enables detection of these processes, based on three parameters that have to be determined before and after the transformation of the landscape: area, perimeter length, and number of patches of the focal landscape class. As an example, the decision tree algorithm is applied to determine the transformation processes of three divergent land cover change scenarios: deciduous woodland degradation in Cadiz Township (Wisconsin, USA) 1831-1950, canopy gap formation in a terra firme rain forest at the Tiputini Biodiversity Station (Amazonian Ecuador) 1997-1998, and forest regrowth in Petersham Township (Massachusetts, USA) 1830-1985. The examples signal the importance of the temporal resolution of the data, since long-term pattern conversions can be subdivided in stadia in which particular pattern components are altered by specific transformation processes.

关键词： decision tree algorithm fragmentation land cover change landscape transformation spatial pattern

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Short term effect evaluation model of rural energy construction revitalization based on ID3 decision tree algorithm

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ENERGY REPORTS 2022年 8卷 1004-1012页

作者： An, Yingbo Zhou, Huasen Appl Technol Res & Dev Ctr Intelligent Finance He Baoding Hebei Peoples R China

In rural energy construction, systematic research is needed in multiple fields including the following: basic frontier investigation on energy resource evaluation and optimal regulation, development of common key technologies, application demonstration and industrialization promotion for the whole chain design, integrated deployment and sub module promotion. Firstly, this paper studies the short-term effect of decision tree algorithm in rural energy construction and revitalization of energy construction. Secondly, through multiple decision tree ID3 algorithms. It combines the advantages of the two algorithms and sets an appropriate threshold for Relief feature selection algorithm and performs attribute complementary screening. Thirdly, this paper establishes a feature selection model based on Relief feature selection algorithm and decision tree ID3 algorithm. The first result is obtained and screened by the decision tree algorithm. During the process, the threshold of the Relief feature selection algorithm is set, the attributes obtained by the Relief feature selection algorithm and the attributes obtained by the decision tree ID3 algorithm are screened and supplemented to realize the screening of irrelevant attributes. By creating the utilization mode of optimal allocation of rural solar energy space, it has a certain promotion and reference value. (C) 2022 The Authors. Published by Elsevier Ltd.

关键词： Energy construction Evaluation model decision tree algorithm Feature selection

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Mechanism Isomorphism Identification Based on decision tree algorithm and Hybrid Particle Swarm Optimization algorithm

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NEURAL PROCESSING LETTERS 2024年第6期56卷 249页

作者： Yu, Luchuan Zhou, Shunqing Wang, Hongbin Wenzhou Univ Coll Mech & Elect Engn Wenzhou 325035 Peoples R China

Mechanism isomorphism identification is a typical quadratic assignment problem similar to traveling salesman and job-shop scheduling. For the complex mechanism with more components, common methods of isomorphism identification may fail due to low solving efficiency and reliability. Based on the decision tree algorithm and hybrid particle swarm optimization (HPSO) algorithm, the global-local search method is proposed to identify isomorphism of mechanisms. More precisely, based on the intrinsic relationship between links and vertices in the mechanism, the decision tree algorithm globally searches the characteristic path with mapping properties of different mechanisms. On this basis, HPSO algorithm combines genetic algorithm with particle swarm optimization algorithm to find the exact global optimal solution instead of local optimal solution. Some complex cases such as 14-link kinematic chains, 18-vertex topological graphs, and 8-vertex planetary gear trains are used to evaluate the efficiency and reliability of the proposed method. Results show that the proposed method can accurately identify isomorphism of mechanisms in a relatively short time. It can improve the solving efficiency of isomorphism identification in structural synthesis.

关键词： Isomorphism identification Adjacency matrix Hybrid particle swarm optimization algorithm decision tree algorithm Kinematic chain

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College public sports culture practice based on decision tree algorithm

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PERSONAL AND UBIQUITOUS COMPUTING 2020年第2期24卷 207-221页

作者： Xu, Shuping Liang, Lixin Ji, Chengbin North China Elect Power Univ Beijing 102206 Peoples R China Beijing Coll Polit & Law Beijing 102628 Peoples R China

The public sports culture of colleges is based on the basic skills and strategies of the public sports culture curriculum. The study of public sports culture in colleges focuses on the unity and standardization of teaching forms, structures, contents, methods, assessments, and evaluations. This paper considers the various links that affect the public sports culture of colleges, identifies frequent item sets, and gains support by establishing support and confidence thresholds. The frequent item sets of the degrees and confidence with the rules generated by a decision tree algorithm are compared to identify the key factors that affect the actual effect. This paper fully considers the public sports culture of colleges to comprehensively analyze the relevant factors, verify and compare the rules generated by the decision tree algorithm, and identify the key factors that affect the actual effect. By an example verification, the method of this paper has certain guiding value for the study of public sports culture.

关键词： Frequent item sets decision tree algorithm Public sports culture

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An improved decision tree algorithm based on variable precision neighborhood similarity

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INFORMATION SCIENCES 2022年 615卷 152-166页

作者： Liu, Caihui Lin, Bowen Lai, Jianying Miao, Duoqian Gannan Normal Univ Sch Math & Comp Sci Ganzhou 341000 Jiangxi Peoples R China Tongji Univ Dept Comp Sci & Technol Shanghai 201804 Peoples R China

The decision tree algorithm has been widely used in data mining and machine learning due to its high accuracy, low computational cost and high interpretability. However, when dealing with the continuous data, the classical decision tree algorithm needs to replace continuous attributes with discretized attributes by the strategy of discretization. Discretization may cause a loss of information structure, which will affect the performance of classification. To tackle this problem, many researchers have proposed different decision tree methods based on variable precision neighborhood rough sets. However, these methods do not consider the geometric structure of neighborhood systems, which may lead to a contradiction in the transitivity of the equivalence relation. In this paper, we first define a novel neighborhood geometric similarity in a neighborhood system from the perspective of geometry. Second, by combining the neighborhood geometric similarity and the neighborhood algebraic similarity, we propose four new kinds of neighborhood similarities, which can solve the contradictory transitivity of the equivalence relation. Third, a variable precision neighborhood rough set model is constructed using the new similarities, and a novel decision tree algorithm is proposed based on this model, where the degree of attribute dependence is used as the partition measure. Experimental results on 14 selected datasets from the UCI Machine Learning Repository show that our algorithm is effective. The average accuracy of our algorithm is over 90%, which is 10% higher than the classical decision tree algorithms, and the number of leaf nodes increases slightly. (c) 2022 Elsevier Inc. All rights reserved.

关键词： decision tree algorithm Neighborhood geometry similarity Neighborhood algebraic similarity Equivalence relation Variable precision neighborhood rough set Attribute dependence

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Research on optimization and application of Spark decision tree algorithm under cloud-edge collaboration

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第11期37卷 8833-8854页

作者： Wang, Suzhen Jia, Zhiting Cao, Ning Hebei Univ Econ & Business Shijiazhuang Hebei Peoples R China Wuxi Vocat Coll Sci & Technol Sch Artificial Intelligence Wuxi Jiangsu Peoples R China

This paper optimizes and parallelizes the C4.5 algorithm on the Spark platform under cloud-edge collaboration. First, the information entropy calculation in the definition of boundary points and Fayyad's theorem is improved by the Gini index, which reduces the number of information entropy calculations for selecting segmentation points in the traditional C4.5 algorithm continuous attribute discretization operation and simplifies the calculation formula, thus reducing the execution time of the algorithm. On this basis, the CFS algorithm is introduced to optimize the calculation of the information gain ratio, to facilitate the selection of better attributes for decision tree partitioning. The improved C4.5 algorithm is then parallelized in Spark platform. In this paper, we choose the "intelligent door guard" of cloud-edge collaboration under the epidemic prevention and control management context as the application scenario for experimental verification and make timely risk assessment for those who want to enter the place and give them a response whether they are allowed to enter or not. Experiments show that the improved parallel C4.5 algorithm reduces the running time and raises the accuracy of the algorithm, compared with the traditional C4.5 algorithm and previous improvements to it. In the application scenario of cloud-edge collaboration, the improved algorithm in this paper decreases the time delay and increases the inspection efficiency.

关键词： C4 5 algorithm cloud-edge collaboration decision tree algorithm edge computing information gain ratio

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An improved decision tree algorithm based on boundary mixed attribute dependency

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APPLIED INTELLIGENCE 2024年第2期54卷 2136-2153页

作者： Lin, Bowen Liu, Caihui Miao, Duoqian Gannan Normal Univ Sch Math & Comp Sci Ganzhou 341000 Jiangxi Peoples R China Tongji Univ Dept Comp Sci & Technol Shanghai 201804 Peoples R China

As an effective extension of rough set theory, the variable precision neighborhood rough set model has been applied to the attribute dependency-based improvement of decision tree algorithm of the solution concerning continuous data. However, the boundary region, as an effective description of the uncertainty of knowledge, has not been taken into account in the existing algorithms. In this paper, we define a novel decision rule based on boundary region and attribute dependency, and construct a decision tree algorithm via this decision rule. First, we define a measure called boundary coefficient based on the boundary region, which can be used for comparative quantitative analysis. Second, we define the boundary mixed attribute dependency by combining the boundary coefficient and the attribute dependency, which can consider both the boundary case of the target set and the attribute dependency. Finally, a novel decision tree algorithm is proposed by using the boundary mixed attribute dependency as the decision rule. The experimental results show that with a slight increase in leaf nodes, the total running time decreases and the maximum accuracy increases to 0.9518, which indicates the effectiveness of the proposed algorithm.

关键词： decision tree algorithm Neighborhood similarity Variable precision neighborhood rough sets Boundary coefficient Boundary mixed attribute dependency

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