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2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Li, Guangpeng Zhou, Hengchao Shandong Institute of Commerce and Technology Jinan China

ISBN: (纸本)9798350313413

With the development of deep learning related algorithms, the target requirements of performance recognition models are constantly being optimized. The research on student performance recognition modeling and estimation methods based on xgboost (Extreme Gradient Boosting) algorithm is becoming increasingly important. In the construction of the entire student achievement recognition and estimation model, how to improve the accuracy of the improved model's achievement estimation and the efficiency of the system recognition is currently a key issue that needs to be urgently addressed. This article conducts research on traditional student performance prediction methods, utilizes the application of xgboost algorithm in student performance, analyzes the algorithm process steps, and combines the performance evaluation system of student performance classification. Based on the data results, the following conclusions are drawn through discussion. Six subject samples were selected through simulation experiments, and the student achievement recognition and estimation model based on xgboost algorithm was applied to improve the accuracy of achievement estimation compared to traditional schemes, with a comprehensive average improvement of about 8.4%. At the same time, there have been improvements in the efficiency of system identification, and the overall average improvement is about 17.8%. This indicates that the student achievement recognition and estimation model based on xgboost algorithm has good practical application effect in predicting student achievement. Finally, this study also provides data-based decision-making support for education policy makers, helping to optimize the allocation of educational resources, improve education quality, promote academic success and personal development of students, and thus contribute important methods and tools to the improvement and enhancement of the education system. © 2023 IEEE.

关键词： grade prediction identification estimation student grades xgboost algorithm

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A Data-Driven Method for Power System Transient Instability Mode Identification Based on Knowledge Discovery and xgboost algorithm

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IEEE ACCESS 2021年 9卷 154172-154182页

作者： Zhang, Neng Qian, Huimin He, Yuchao Li, Lirong Sun, Chaoyun Wuhan Nari Ltd Liabil Co State Grid Power Res Inst Wuhan 430000 Peoples R China

Aiming at the difficulty of unstable pattern recognition after power system fault, a novel identification framework for transient instability mode identification based on knowledge discovery by accuracy maximization (KODAMA) and extreme gradient boosting (xgboost) algorithm is proposed. In this method, the transient stability of all typical fault scenarios of power system is obtained firstly by xgboost. Then, to make full use of the structure of the raw data and mine the contained data information, a novel data mining algorithm KODAMA is introduced to cluster the mode of rotor angle in case of instability, thus to convert pattern-unlabeled case data into pattern-labeled data. Finally, based on this labeled data, to fully reflect the dynamic characteristics, a multiple xgboost assessment strategy is designed to recognize different instable modes. The proposed technique is tested on the Nordic test system, and the results indicate that the proposed approach can provide fast and accurate recognition of instable mode and has a certain prospect of online application.

关键词： Power system stability Transient analysis Rotors Generators Stability criteria Clustering algorithms Data models Transient stability KODAMA algorithm xgboost algorithm machine learning

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xgboost algorithm-based prediction of concrete electrical resistivity for structural health monitoring

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AUTOMATION IN CONSTRUCTION 2020年 114卷 103155-000页

作者： Dong, Wei Huang, Yimiao Lehane, Barry Ma, Guowei Univ Western Australia Dept Civil Environm & Min Engn Perth WA 6009 Australia Hebei Univ Technol Dept Civil & Transportat Engn Tianjin 300401 Peoples R China

For structural health monitoring, electrical resistivity measurement (ERM) method is commonly employed for the detection of concrete's durability, as indicated by the chloride permeability and the corrosion of steel reinforcement. However, according to previous experimental studies, ERM results are susceptible to significant uncertainties due to multiple influencing factors such as concrete water/cement ratio and structure curing environment as well as their complex interrelationships. The present study therefore proposes an xgboost algorithm-based prediction model which considers all potential influential factors simultaneously. A database containing 800 experimental instances composed of 16 input attributes is constructed according to existing reported studies and utilized for training and testing the xgboost model. Statistical scores (RMSE, MAE and R-2) and the GridsearchCV feature are applied to evaluate and optimize the established model respectively. Results show that the proposed xgboost model achieves satisfactory predictive performance as demonstrated by high coefficients of regression fitting lines (0.991 and 0.943) and comparatively low RMSE values (4.6 and 11.3 k5 Omega cm) for both training and testing sets respectively. The analyses of the attribute importance ranking also reveal that curing age and cement content have the greatest influence on ERM results.

关键词： Concrete electrical resistivity Structural health monitoring Machine learning xgboost algorithm

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Classification of earthquakes, explosions and mining-induced earthquakes based on xgboost algorithm

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COMPUTERS & GEOSCIENCES 2023年 170卷

作者： Wang, Tingting Bian, Yinju Zhang, Yixiao Hou, Xiaolin China Earthquake Adm Inst Geophys Beijing 100081 Peoples R China

The classification of low-magnitude tectonic earthquakes, explosions and mining-induced earthquakes is an important task in regional earthquake monitoring. Seismic events occurring at local and regional distances are classified primarily based on the characteristics of their waveform. We established 36-dimensional and 201dimensional datasets by using feature extraction and amplitude spectral analysis. The Extreme Gradient Boosting (xgboost) supervised algorithm is introduced for the discrimination of couples-class and three-class. The accuracies in the earthquakes/explosions discrimination with feature extraction dataset and amplitude spectrum dataset are 97.48% and 95.12%, respectively, which shows that feature extraction can effectively quantify the differences between earthquakes and explosions. For the classification of earthquakes/mininginduced earthquakes and explosions/mining-induced earthquakes, the performance of xgboost with the amplitude spectrum dataset is greater, with accuracies of 99.24% and 95.33%, respectively. In the classification of the three types of events, the accuracies of xgboost are 96.41% for earthquakes, 90.38% for explosions, and 94.04% for mining-induced earthquakes. The performance indices of xgboost for different input parameters are invariably greater than those of the support vector machine (SVM), with stable classification ability, suggesting that the xgboost model has good prospects for application in seismic event classification.

关键词： Seismic events identification xgboost algorithm Feature extraction Model performance index

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A Hybrid ENSO Prediction System Based on the FIO-CPS and xgboost algorithm

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REMOTE SENSING 2023年第7期15卷 1728页

作者： Kuang, Zhiyuan Song, Yajuan Wu, Jie Fu, Qiuying Shu, Qi Qiao, Fangli Song, Zhenya Minist Nat Resources Inst Oceanog 1 Qingdao 266061 Peoples R China Minist Nat Resources Key Lab Marine Sci & Numer Modeling Qingdao 266061 Peoples R China Shandong Key Lab Marine Sci & Numer Modeling Qingdao 266061 Peoples R China Natl Climate Ctr China Meteorol Adm Key Lab Climate Predict Studies Beijing 100081 Peoples R China

Accurate prediction of the El Nino-Southern Oscillation (ENSO) is crucial for climate change research and disaster prevention and mitigation. In recent decades, the prediction skill for ENSO has improved significantly;however, accurate forecasting at a lead time of more than six months remains challenging. By using a machine learning method called eXtreme Gradient Boosting (xgboost), we corrected the ENSO predicted results from the First Institute of Oceanography Climate Prediction System version 2.0 (FIO-CPS v2.0) based on the satellite remote sensing sea surface temperature data, and then developed a dynamic and statistical hybrid prediction model, named FIO-CPS-HY. The latest 15 years (2007-2021) of independent testing results showed that the average anomaly correlation coefficient (ACC) and root mean square error (RMSE) of the Nino3.4 index from FIO-CPS v2.0 to FIO-CPS-HY for 7- to 13-month lead times could be increased by 57.80% (from 0.40 to 0.63) and reduced by 24.79% (from 0.86 degrees C to 0.65 degrees C), respectively. The real-time predictions from FIO-CPS-HY indicated that the sea surface state of the Nino3.4 area would likely be in neutral conditions in 2023. Although FIO-CPS-HY still has some biases in real-time prediction, this study provides possible ideas and methods to enhance short-term climate prediction ability and shows the potential of integration between machine learning and numerical models in climate research and applications.

关键词： ENSO prediction FIO-CPS v2.0 machine learning hybrid prediction xgboost algorithm

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A study on predicting the length of hospital stay for Chinese patients with ischemic stroke based on the xgboost algorithm

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2023年第1期23卷 49-49页

作者： Chen, Rui Zhang, Shengfa Li, Jie Guo, Dongwei Zhang, Weijun Wang, Xiaoying Tian, Donghua Qu, Zhiyong Wang, Xiaohua Cangzhou Cent Hosp Refined Management Off Cangzhou Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Natl Populat Heath Data Ctr Beijing Peoples R China Hebei Univ Technol Sch Econ & Management Tianjin Peoples R China Beijing Normal Univ Sch Social Dev & Publ Policy Beijing Peoples R China

BackgroundThe incidence of stroke is a challenge in China, as stroke imposes a heavy burden on families, national health services, social services, and the economy. The length of hospital stay (LOS) is an essential indicator of utilization of medical services and is usually used to assess the efficiency of hospital management and patient quality of care. This study established a prediction model based on a machine learning algorithm to predict ischemic stroke patients' *** total of 18,195 ischemic stroke patients' electronic medical records and 28 attributes were extracted from electronic medical records in a large comprehensive hospital in China. The prediction of LOS was regarded as a multi classification problem, and LOS was divided into three categories: 1-7 days, 8-14 days and more than 14 days. After preprocessing the data and feature selection, the xgboost algorithm was used to build a machine learning model. Ten fold cross-validation was used for model validation. The accuracy (ACC), recall rate (RE) and F1 measure were used to evaluate the performance of the prediction model of LOS of ischemic stroke patients. Finally, the xgboost algorithm was used to identify and remove irrelevant features by ranking all attributes based on feature *** with the naive Bayesian algorithm, logistic region algorithm, decision tree classifier algorithm and ADaBoost classifier algorithm, the XGBoot algorithm has higher ACC, RE and F1 measure. The average ACC, RE and F1 measure were 0.89, 0.89 and 0.89 under the 10-fold cross-validation. According to the analysis of the importance of features, the LOS of ischemic stroke patients was affected by demographic characteristics, past medical history, admission examination features, and operation characteristics. Finally, the features in terms of hemiplegia aphasia, MRS, NIHSS, TIA, Operation or not, coma index etc. were found to be the top features in importance in predicting the LOS of ischemic stroke

关键词： Ischemic stroke xgboost algorithm Length of hospital stay (LOS) Machine learning (ML) model

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Modal Parameter Identification of Civil Engineering Structure Based on xgboost algorithm

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Procedia Computer Science 2023年 228卷 446-454页

作者： Xuguang Zhang Zhiying Tian College of Architecture and Civil Engineering Chongqing Metropolitan College of Science and Technology Chongqing China

In order to prevent the structural problems of buildings and related civil engineering from becoming potential risks, the modal parameter identification method of civil engineering structures is studied based on xgboost algorithm. First of all, it is necessary to design and extract the modal parameters of civil engineering structures, and then detect civil engineering structures according to the modal parameter identification model, identify the existing problems of the structures, discuss and analyze them, verify the effect of the detection method of civil engineering structures based on modal parameter identification, and make a comparative analysis with the traditional modal parameter identification methods such as SSI. By analyzing the basic principle and algorithm of xgboost algorithm, this study analyzes the research characteristics and present situation of the method, identifies the implementation and feasibility of modal parameters of civil engineering structures, and provides theoretical basis and data reference for the development of related professions and related industries.

关键词： xgboost algorithm Civil Engineering Structural Mode Parameter Identification Modal of Civil Engineering Structure

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The Evaluation of Color Spaces for Large Woody Debris Detection in Rivers Using xgboost algorithm

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REMOTE SENSING 2022年第4期14卷

作者： Liang, Min-Chih Tfwala, Samkele S. Chen, Su-Chin Natl Chung Hsing Univ Dept Soil & Water Conservat 145 Xingda Rd Taichung 40227 Taiwan Univ Eswatini Dept Geog Environm Sci & Planning M201 Kwaluseni Eswatini Natl Chung Hsing Univ Innovat & Dev Ctr Sustainable Agr IDCSA 145 Xingda Rd Taichung 40227 Taiwan

Large woody debris (LWD) strongly influences river systems, especially in forested and mountainous catchments. In Taiwan, LWD are mainly from typhoons and extreme torrential events. To effectively manage the LWD, it is necessary to conduct regular surveys on river systems. Simple, low cost, and accurate tools are therefore necessary. The proposed methodology applies image processing and machine learning (xgboost classifier) to quantify LWD distribution, location, and volume in river channels. xgboost algorithm was selected due to its scalability and faster execution speeds. Nishueibei River, located in Taitung County, was used as the area of investigation. Unmanned aerial vehicles (UAVs) were used to capture the terrain and LWD. Structure from Motion (SfM) was used to build high-resolution orthophotos and digital elevation models (DEM), after which machine learning and different color spaces were used to recognize LWD. Finally, the volume of LWD in the river was estimated. The findings show that RGB color space as LWD recognition factor suffers serious collinearity problems, and it is easy to lose some LWD information;thus, it is not suitable for LWD recognition. On the contrary, the combination of different factors in different color spaces enhances the results, and most of the factors are related to the YCbCr color space. The CbCr factor in the YCbCr color space was best for identifying LWD. LWD volume was then estimated from the identified LWD using manual, field, and automatic measurements. The results indicate that the manual measurement method was the best (R-2 = 0.88) to identify field LWD volume. Moreover, automatic measurement (R-2 = 0.72) can also obtain LWD volume to save time and workforce.

关键词： unmanned aerial vehicles (UAVs) large woody debris color space model xgboost algorithm LWD volume estimation

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xgboost algorithm under Differential Privacy Protection

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Journal of Information Hiding and Privacy Protection 2021年第1期3卷 9-16页

作者： Yuanmin Shi Siran Yin Ze Chen Leiming Yan School of Computer&Software Nanjing University of Information Science&TechnologyNanjingChina Engineering Research Center of Digital Forensics Ministry of EducationNanjingChina

Privacy protection is a hot research topic in information security *** improved xgboost algorithm is proposed to protect the privacy in classification *** combining with differential privacy protection,the xgboost can improve the classification accuracy while protecting privacy *** using CART regression tree to build a single decision tree,noise is added according to Laplace *** with random forest algorithm,this algorithm can reduce computation cost and prevent overfitting to a certain *** experimental results show that the proposed algorithm is more effective than other traditional algorithms while protecting the privacy information in training data.

关键词： Differential privacy privacy protection xgboost algorithm CART regression tree

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Identification of Mine Mixed Water Inrush Source Based on Genetic algorithm and xgboost algorithm: A Case Study of Huangyuchuan Mine

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WATER 2022年第14期14卷

作者： Li, Xiang Dong, Donglin Liu, Kun Zhao, Yi Li, Minmin China Univ Min & Technol Beijing Coll Geosci & Surveying Engn Beijing 100083 Peoples R China Minist Nat Resources Land Satellite Remote Sensing Applicat Ctr Beijing 100048 Peoples R China

Mine water inrush disaster seriously threatens the production of coal mine. Rapid and accurate identification of mine water inrush sources is a key premise for mine water disaster prevention. The conventional research on the identification of water inrush source has focused on a single source, and the identification of mixed water samples from multi-source aquifers in deep coal mining environment is not yet fully explored. In this study, absorption spectrum technology was introduced into the identification of water inrush sources. The absorption spectra of the water samples with different mixing ratios were prepared using the ultraviolet and visible spectrophotometry (UV-Vis) spectrophotometer. In addition, spectral data preprocessing such as scattering correction, baseline correction, smoothing and denoising, and data enhancement were conducted to reduce the influence of experimental error, environment, radiation, molecular interaction, and other factors on the spectral data. Furthermore, a genetic algorithm (GA) was used to improve the seven parameters of the extreme gradient boosting (xgboost) algorithm, such as learning rate, base model selection, tree parameters, regularization parameters, and iteration times. The deep-learning classifier of mine mixed water sources based on GA-xgboost was established and used to identify 66 groups of mixed water sources in the Huangyuchuan Mine. The simulation results show that spectral preprocessing and normalization enhancement effectively improved the accuracy of the discriminant model. After 100 cross-validations, the average recognition accuracy of the GA-xgboost model was 94%, and the results were accurate and reliable. This study provides a new direction and method for the identification of water inrush sources, particularly for mixed water inrush sources. It may also serve as a technical reference for decision-makers to formulate effective coal mine water inrush prevention and control programs and for mine water disast

关键词： absorption spectrum mixed water source genetic algorithm xgboost algorithm

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