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

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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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The Promotion of Women's Leisure Sports Behavior Based on Improved decision tree algorithm

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INTERNATIONAL JOURNAL OF INTELLIGENT INFORMATION TECHNOLOGIES 2024年第1期20卷 1页

作者： Luo, Huaping Hunan Coll Chem Technol Zhuzhou Peoples R China

In women's daily leisure choices, sports is an important content that cannot be ignored. In this context, this paper studies the promotion of women's leisure sports behavior based on improved decision tree algorithm. Based on the simple analysis of the research progress of leisure sports and decision tree algorithm, a female leisure sports behavior model based on decision tree is constructed. Based on the decision tree algorithm, the calculation method of information gain rate is optimized to avoid logarithmic operation, and the continuous attributes are discretized. Simulation results show that in terms of classification accuracy, the improved decision tree algorithm is significantly higher than the classical decision tree algorithm, and can significantly shorten the running time, which has high application value in the realization of accurate classification analysis of female leisure sports behavior.

关键词： decision tree algorithm Female Leisure Sports Behavior Information Gain Rate Taylor Formula

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A dynamic evaluation of MOOC online English teaching based on decision tree algorithm

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INTERNATIONAL JOURNAL OF CONTINUING ENGINEERING EDUCATION AND LIFE-LONG LEARNING 2024年第2-3期34卷 167页

作者： Qian, Lu Jiangxi Police Coll Dept Publ Secur Management Nanchang 330199 Jiangxi Peoples R China

Aiming at the problems of low evaluation accuracy, complex evaluation process and time-consuming evaluation, this paper designed a dynamic evaluation method of MOOC online English teaching based on decision tree algorithm. Firstly, the data type is determined, and the correlation and correlation degree of the data are determined by Chi-square statistics method and mutual information method. Then, the redundant data and noise are removed by calculating the data centroid distance in the data set. Finally, the attribute value of evaluation decision tree is determined by decision tree, the evaluation model is constructed, and the evaluation error is corrected by decision tree pruning method to achieve dynamic evaluation of MOOC online English teaching. Experimental results show that the proposed method has the highest accuracy of 97% and takes 1.6 s, which effectively improves the evaluation efficiency.

关键词： Chi-square statistical method relevance decision tree algorithm MOOC online English teaching dynamic evaluation suppress noise

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Classification method of English micro-course resources on MOOC platform based on improved decision tree algorithm

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2024年第1-2期74卷 34-43页

作者： Wang, Juan Wuhan Tech Coll Commun Wuhan Hubei Peoples R China

To solve the problem of inaccurate classification results of current educational resource classification methods, a MOOC micro-class resource classification method based on improved decision tree algorithm is proposed. Firstly, adaptive reconstruction technology and feature sequence detection technology are used to build a storage structure model for optimising unbalance data of cloud resource distribution space on MOOC platform. Then, the information gain rate of each attribute is determined by using the information entropy of each attribute and the decision tree is constructed. Finally, the improved information entropy is used to optimise the decision tree to realise the classification of English micro-lesson resources. The experimental results show that the AUC value of the classification results of the proposed method is higher than 0.85, and the precision rate, recall rate and F-scale value are higher than 0.7, which indicates a high accuracy of resource classification.

关键词： decision tree algorithm MOOC platform English micro-course resources classification method information entropy information gain rate

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Forecasting Uncertainty Parameters of Virtual Power Plants Using decision tree algorithm

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2023年第16期51卷 1756-1769页

作者： Krishna, Raji Sathish, Hemamalini Zhou, Ning Vellore Inst Technol Sch Elect Engn Chennai Tamil Nadu India SUNY Binghamton ECE Dept New York NY USA

Renewable energy sources (RESs) are increasingly used to meet the world's growing electrical needs, especially for the economic benefits and environmental problems associated with fossil fuel use. Small-scale renewable energy sources, controllable loads, energy storage devices, and other nonrenewable sources are effectively integrated to form a virtual power plant (VPP). Uncertainty in forecasting renewable energy generation due to the intermittent nature of renewable energy sources is one of the biggest challenges in VPPs. Power generation by RESs changes with the day of the week, season, location, climate, and resource availability. Also, load demand and utility price vary with time and need to be forecasted for proper energy management of VPPs. However, the dispatching and planning of VPPs are significantly impacted by the volatile nature of RESs, load demand, and utility price. Predicting these uncertainties with high accuracy is essential to balance the electrical power generation and the load demand. In this article, a decision tree (DT) algorithm is proposed, to predict the uncertainty parameters, such as the day-ahead power from the RES, load demand, and utility prices of VPPs. The efficiency of the proposed model and the predicted results are compared with other complex models, such as the artificial neural network (ANN) and auto-regressive integrated moving average (ARIMA) algorithms. Root-mean square error (RMSE), mean square error (MSE), coefficient of determination (R-2), and mean absolute error (MAE) are the statistical metrics used to evaluate the accuracy of the prediction. One-year meteorological data of the Chennai zone in India is considered for predicting the uncertainty parameters. IEEE 16-bus and 33-bus test systems are used to validate the forecasting model. It is evident from the results that the proposed DT algorithm can predict the uncertainty parameters more accurately and use lesser time than the ANN and ARIMA algorithms.

关键词： forecasting uncertainty parameters renewable energy sources distributed generators machine learning decision tree algorithm ANN algorithm ARIMA algorithm statistical metrics

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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 Northeast Elect Power Univ Sports Inst Coll Jilin 132012 Peoples R China Minzu Univ China Sports Inst Coll Beijing 100036 Peoples R 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.

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

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Application of decision tree algorithm on Landsat 9 OLI-2 Images for coastline extraction

Application of Decision Tree algorithm on Landsat 9 OLI-2 Im...

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2024 International Workshop on Metrology for the Sea Learning to Measure Sea Health Parameters

作者： Amoroso, Pier Paolo Parente, Claudio Ciaramella, Angelo Ferone, Alessio Staiano, Antonino Parthenope Univ Naples Ctr Direz Dept Sci & Technol Isola C4 I-80143 Naples Italy

ISBN: (纸本)9798350379013;9798350379006

The extraction of the coastline from aerial and satellite images constitutes a basic task of remote sensing that finds a powerful operational tool in Machine Learning techniques. The various algorithms present in the literature, such as K-Means, decision tree (DT), Support Vector Machine, can be applied directly to one of the available multispectral bands or to a combination of them;alternatively, two or more bands can be previously processed using specific indices aimed at highlighting the different spectral response of water pixels compared to others of a different nature, i.e. vegetation and/or bare soil, present in the analyzed scene. This paper aims to verify the effectiveness of the DT algorithm applied to satellite Landsat 9 OLI multispectral imagery concerning a large part of the Tyrrhenian Calabrian coast (Italy). Specifically the following datasets are considered: Near Infrared (NIR) band, RGB true color composition (RGB), combination of RGB and NIR (RGB+NIR), Normalize Difference Vegetation Index (NDVI), Normalize Difference Water Index (NDWI), Modified Difference Water Index (MNDWI), SWIR Minus Blue Index (SMBI). DT is run on MATLAB, while all remaining operations are performed using Q-GIS software. The extracted coastlines are compared with the reference one resulting from manual vectorization to establish the most performing approach. The best result is derived by DT applications to MNDWI.

关键词： coastline extraction machine learning supervised classification decision tree algorithm satellite images remote sensing

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Construction of a Student Learning Behavior Prediction Model Based on decision tree algorithm 24

Construction of a Student Learning Behavior Prediction Model...

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International Conference on Modeling, Natural Language Processing and Machine Learning (CMNM)

作者： Zhang, Guifang Wuhan Technol & Business Univ Wuhan 430065 Peoples R China

ISBN: (纸本)9798400709760

Diagnosis, monitoring, and evaluation of learning behavior have always been the focus of research in Educational Data Mining (EDM). Predicting student academic performance based on online learning behavior data for early intervention in student learning is a key issue that urgently needs to be addressed. The learning behavior of students can reflect the state of the learning process, the quality of teaching, and whether the school's management methods are good. Studying it can identify problems in educational work and propose corresponding solutions. This article constructs a student learning behavior prediction model based on decision tree (DT) algorithm. After reading data from the school data center, preprocessing is performed, and abnormal data is removed before DM and analysis. The results are obtained by comparing student behavior data with student grade points, providing more comprehensive support for university management services. The model can predict students' grades at the end of the course or predict whether they may face learning difficulties and require additional assistance. The experimental results indicate that the model proposed in this article helps to improve the learning effectiveness and experience of students, while also providing valuable teaching aids for teachers and educational institutions.

关键词： decision tree algorithm Student learning behavior prediction model

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