检索结果-内蒙古大学图书馆

An Improved Genetic-xgboost Classifier for Customer Consumption Behavior Prediction

COMPUTER JOURNAL 2023年第3期67卷 1041-1059页

作者： Li, Yue Qi, Jianfang Jin, Haibin Tian, Dong Mu, Weisong Feng, Jianying China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Minist Agr Key Lab Viticulture & Enol Beijing 100083 Peoples R China

In an increasingly competitive market, predicting the customer's consumption behavior has a vital role in customer relationship management. In this study, a new classifier for customer consumption behavior prediction is proposed. The proposed methods are as follows: (i) A feature selection method based on least absolute shrinkage and selection operator (Lasso) and Principal Component Analysis (PCA), to achieve efficient feature selection and eliminate correlations between variables. (ii) An improved genetic-eXtreme Gradient Boosting (xgboost) for customer consumption behavior prediction, to improve the accuracy of prediction. Furthermore, the global search ability and flexibility of the genetic mechanism are used to optimize the xgboost parameters, which avoids inaccurate parameter settings by manual experience. The adaptive crossover and mutation probabilities are designed to prevent the population from falling into the local extremum. Moreover, the grape-customer consumption behavior dataset is employed to compare the six Lasso-based models from the original, normalized and standardized data sources with the Isometric Mapping, Locally Linear Embedding, Multidimensional Scaling, PCA and Kernel Principal Component Analysis methods. The improved genetic-xgboost is compared with several well-known parameter optimization algorithms and state-of-the-art classification approaches. Furthermore, experiments are conducted on the University of California Irvine datasets to verify the improved genetic-xgboost algorithm. All results show that the proposed methods outperform the existing ones. The prediction results provide the decision-making basis for enterprises to formulate better marketing strategies.

关键词： Lasso model feature selection genetic mechanism xgboost algorithm consumption behavior prediction

来源：评论

学校读者我要写书评

暂无评论

Landslide Hazard Assessment in Highway Areas of Guangxi Using Remote Sensing Data and a Pre-Trained xgboost Model

引用

REMOTE SENSING 2023年第13期15卷 3350-3350页

作者： Zhang, Yuze Deng, Lei Han, Ying Sun, Yunhua Zang, Yu Zhou, Minlu China Transport Telecommun & Informat Ctr Natl Engn Res Ctr Transportat Safety & Emergency I Beijing 100028 Peoples R China Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China State Grid Siji Locat Serv Co Ltd Beijing 102200 Peoples R China GuangXi Commun Design Grp Co Ltd Nanning 530012 Peoples R China

This study presents a novel method for assessing landslide hazards along highways using remote sensing and machine learning. We extract geospatial features such as slope, aspect, and rainfall over Guangxi, China, and apply an extreme gradient boosting model pre-trained on contiguous United States datasets. The model produces susceptibility maps that indicate landslide probability at different scales. However, the lack of accurate data on historical landslides in Guangxi challenges the model evaluation and comparison between regions. To overcome this, we calibrate the model to fit the local conditions in Guangxi. The calibrated model agrees with the observed landslide locations, implying its capability to capture regional variations in landslide mechanisms. We apply the model at a 30 m resolution along the Heba Expressway and validate it against reports from July 2021 to March 2022. The model correctly predicts five of seven landslide events in this period with a reasonable alarm rate. This framework has the potential for large-scale landslide risk management by informing transportation planning and infrastructure maintenance decisions. More data on landslide timing and human disturbance events may improve the model's accuracy across diverse geographical areas and terrains.

关键词： landslide hazard assessment remote sensing data xgboost algorithm highway pre-trained model

来源：评论

学校读者我要写书评

暂无评论

Predicting ESG disclosure quality through board secretaries' characteristics: A machine learning approach

引用

RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE 2025年 76卷

作者： Yang, Jie Niu, Yanfang Shi, Wenlei Zhu, Kanghuan Taizhou Vocat & Tech Coll Sch Management Taizhou 318000 Peoples R China Shandong Univ Finance & Econ Sch Accountancy Jinan 250014 Peoples R China

This study employs a machine learning approach to explore the relationship between board secretaries' (BS') characteristics and the quality of Environmental, Social, and Governance (ESG) disclosure in enterprises. The results indicate that BS' competence characteristics are the primary drivers of ESG disclosure quality, with salary, functional experience, age, and tenure as key predictive features. BS' salary and functional experience show an upward trend with ESG disclosure quality. BS' age has a nonlinear predictive effect on ESG disclosure quality, exhibiting an approximately positive "U"-shaped relationship. BS' tenure also demonstrates a nonlinear predictive effect. Robustness checks confirm that BS' characteristics remain crucial factors even after controlling for CEO characteristics. Interaction effect analysis reveals that the impact of BS' salary, functional experience, and tenure on ESG disclosure quality is more significant among middle-aged board secretaries. Heterogeneity tests based on ownership type, institutional environment, and media attention show that the importance of BS' characteristics varies across different contexts. This study provides a new perspective and empirical evidence for predicting ESG disclosure quality, offering guidance for improving the appointment process and incentive mechanisms for board secretaries.

关键词： ESG disclosure quality Board secretaries' characteristics Event prediction Machine learning xgboost algorithm

来源：评论

学校读者我要写书评

暂无评论

Improved estimation of daily blue-sky snow shortwave albedo from MODIS data and reanalysis information

引用

SCIENCE OF REMOTE SENSING 2024年 10卷

作者： Ding, Anxin Liang, Shunlin Ma, Han He, Tao Jia, Aolin Wang, Qian Hefei Univ Technol Sch Resources & Environm Engn Hefei 230009 Peoples R China Univ Hong Kong Dept Geog Jockey Club STEM Lab Quantitat Remote Sensing Hong Kong 999077 Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Hubei Key Lab Quantitat Remote Sensing Land & Atmo Wuhan 430079 Peoples R China Luxembourg Inst Sci & Technol LIST Dept Environm Res & Innovat ERIN L-4362 Belvaux Luxembourg Beijing Normal Univ Fac Geog Sci State Key Lab Remote Sensing Sci Beijing 100875 Peoples R China

Snow albedo is a key geophysical parameter that controls the energy exchanges between the atmosphere and Earth's surfaces and has been widely utilized in climatic and environmental change studies. However, recent studies have demonstrated that current albedo satellite products still have large uncertainties in snow-covered areas. In this study, we estimated the blue-sky shortwave albedo of snow surfaces using the eXtreme Gradient Boosting (xgboost) algorithm with Moderate Resolution Imaging Spectroradiometer (MODIS) top-of-atmosphere (TOA) reflectance values, ERA-5 land reanalysis snow parameters (e.g., snow cover, snow density and snow depth water equivalent) and in situ measurements. In the xgboost model, the MODIS MCD43 albedo values were input as prior knowledge, and the random sample validation results showed that the R2 2 and root mean square error (RMSE) values of this model were approximately 0.953 and 0.044, respectively. The typical sites for independent validation were subjected to in situ measurements at the UPE_L, AWS5, and CA_ARB sites. Finally, the retrieved xgboost albedo values were compared with the official NASA MODIS (MCD43, collection 6), the Global Land Surface Satellite (GLASS), and the National Oceanic and Atmospheric Administration (NOAA) Visible Infrared Imaging Radiometer Suite (VIIRS) SURFALB albedo products. The validation results indicated that the proposed approach achieved much greater accuracy (RMSE = 0.052, bias = 0.002) than did the corresponding official MODIS (RMSE = 0.087, bias =-0.033), GLASS (RMSE = 0.089, bias =-0.031) and VIIRS SURFALB albedo (RMSE = 0.100, bias =-0.032) products. The improved shortwave albedo captured the rapid temporal changes in surface snow conditions.

关键词： MODIS GLASS VIIRS ERA5-Land Snow albedo Prior knowledge Direct estimation algorithm xgboost algorithm

来源：评论

学校读者我要写书评

暂无评论

Enhancing Cybersecurity With P-Code Analysis and xgboost: A Novel Approach for Malicious VBA Macro Detection in Office Documents

引用

IEEE Access 2024年 12卷 71746-71760页

作者： Ahmadi, Candra Chen, Jiann-Liang Lai, Yi-Cheng National Taiwan University of Science and Technology Department of Electrical Engineering Taipei 106335 Taiwan

In the evolving landscape of cybersecurity, the prevalence of malicious Visual Basic for Applications (VBA) macros embedded in Office documents presents a formidable challenge. These macros, while integral to automation, have become potent vehicles for cyber-attacks, necessitating advanced detection techniques. This study introduces a comprehensive framework employing P-Code Analysis and xgboost, a leading-edge machine learning algorithm, to address this issue. The proposed solution synergizes static analysis of VBA source code with dynamic P-Code structural analysis, enhanced by Natural Language Processing (NLP) techniques for effective feature extraction. By integrating these methodologies, our model adeptly distinguishes between benign and malicious macros, achieving an unprecedented detection accuracy of 98.70% and an F1-score of 98.81% in rigorous testing environments. The core contribution of this research lies in its innovative approach to malicious macro detection, offering a robust framework that significantly improves upon existing methods. Additionally, the utilization of xgboost for machine learning analysis introduces a novel application in cybersecurity defenses against macro-based threats. The results underscore the efficacy of combining P-Code analysis with machine learning for cybersecurity, marking a significant stride in the detection of sophisticated cyber threats. This study not only advances the domain of cybersecurity but also lays the groundwork for future research, advocating for the exploration of further optimizations and the adaptation of our model to combat evolving attack vectors. Recommended terms: Cybersecurity, Malicious VBA Macro Detection, P-Code Analysis, xgboost, Machine Learning. © 2013 IEEE.

关键词： Cybersecurity malicious code detection natural language processing (NLP) office documents security P-code analysis visual basic for applications (VBA) xgboost algorithm

来源：评论

学校读者我要写书评

暂无评论

Research on the Load_breast_cancer data set under Multiple Machine Learning algorithms 24

Research on the Load_breast_cancer data set under Multiple M...

引用

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

作者： Song, Meirui Wu, Shixiao Huang, Rujuan Wuhan Business Univ Wuhan Peoples R China

ISBN: (纸本)9798400709760

breast cancer ranks first in female malignant tumors. Early detection and diagnosis is the key to treatment. This paper uses the open-source load_break_cancer breast cancer data set, mainly uses random forest, support vector machine, logical regression, Gauss naive Bayesian algorithm, BP neural network algorithm, k-neighborhood algorithm and xgboost algorithm to classify and predict the breast cancer data set, conducts a lot of training and testing on the data set under a variety of machine learning algorithms, analyzes the learning curve in the training process, analyzes the training and testing results, and analyzes the performance of the algorithm processing data, which is of great significance for breast cancer diagnosis and treatment.

关键词： Random Forest algorithm Support Vector Machine algorithm Logistic Regression algorithm Gaussian Naive Bayes algorithm BP Neural Network algorithm k-Neighbor algorithm xgboost algorithm

来源：评论

学校读者我要写书评

暂无评论

A Federated MRI and ML Approach for Precision Healthcare Detection 59

A Federated MRI and ML Approach for Precision Healthcare Det...

引用

59th Annual IEEE International Conference on Communications (IEEE ICC)

作者： Almarar, Noof Otoum, Safa Zayed Univ Coll Technol Innovat Abu Dhabi U Arab Emirates

ISBN: (纸本)9781728190549

In the context of neurodegenerative illnesses, including Alzheimer's disease (neuroAD), this study employs simulated Federated Learning (FL) techniques to explore decentralized model training and the application of Machine Learning (ML) algorithms, specifically Random Forest and xgboost, for neuroAD detection. The research demonstrates promising results, with Random Forest achieving an average recall and accuracy of 94.19%, and xgboost outperforming with an average recall and accuracy of 95.53% within a FL framework. These findings highlight the potential of ML in early AD diagnosis. Additionally, this study contributes to the broader field of research on the application of ML in healthcare and provides valuable insights into AD and the identification of other diseases. A limitation faced in this research is the use of a desktop computer with high capacity resources since laptop resources are not enough. The study utilizes a public dataset from Kaggle's "Best Alzheimer's MRI Dataset" to support its findings.

关键词： Machine Learning (ML) algorithms Federated Learning (FL) Random Forest algorithm xgboost algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimization seismic resilience: a machine learning approach for vertical irregular buildings

引用

Asian Journal of Civil Engineering 2024年第8期25卷 6233-6248页

作者： SALAMA, Ahmed Hamed El-Sayed Civil Engineering Department Faculty of Engineering Ajloun National University Ajloun 26810 Jordan Civil Engineering Department Al-Azhar University Nasr City Cairo 11884 Egypt

The paper is a landmark in earthquake and structural engineering, with modern machine-learning techniques applied to introduce innovative investigations into forecasting seismic behavior for vertically uneven structures using sophisticated machine-learning methodologies. The research constructs a very accurate model for making predictions using the xgboost algorithm with the Owl Search algorithm (OSA) for hyperparameter tuning, which explicitly considers complex behavior in the structures under seismic stresses. The variety within the dataset is broad and covers all kinds of irregularities in the structures, such as stiffness and mass irregularities;thus, it has been used to accurately represent the complex characteristics of actual buildings. The results indicate a strong dependence of base shear capacity and seismic performance on the irregularity of stiffness and mass. The test accuracy of the optimized xgboost model was 98.8%. The result was better than that of conventional models, thus proving the effectiveness of integrating the Owl Search algorithm in further fine-tuning the parameters. These results give new variables as insight into affecting earthquake resilience and represent practical applications that enhance building design and retrofitting processes. This is further underlined by the proposal of future research directions that would extend the model’s applicability to other structural anomalies and include additional machine-learning methodologies. Through AI-driven approaches, this study captured complicated structural dynamics with the utmost precision, thus opening new insights that could be brought into practice to improve building design and retrofitting strategies in a way that would diminish the impact of seismic events. Graphical abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Machine learning Optimization Owl search algorithm (OSA) Seismic resilience Vertical Irregular buildings xgboost algorithm

来源：评论

学校读者我要写书评

暂无评论

The Power of Words: Predicting Stock Market Returns with Fine-Grained Sentiment Analysis and xgboost

The Power of Words: Predicting Stock Market Returns with Fin...

引用

Intelligent Systems Conference (IntelliSys)

作者： Balaneji, Farshid Maringer, Dietmar Spasic, Irena Univ Basel Fac Business & Econ Peter Merian Weg 6 CH-4002 Basel Switzerland Cardiff Univ Sch Comp Sci & Informat Abacws Senghennydd Rd Cardiff Wales

ISBN: (纸本)9783031477201;9783031477218

This study investigates the relationship between news sentiment and the stock market's return. The sentiment was automatically analyzed using four methods, including lexicon-based and deep learning-based approaches, at three levels of granularity, i.e., sentence, paragraph, and full text. The sentiment was combined with features from the calendar year, lagged returns, and news publishers, which were fed into the xgboost algorithm trained to classify the direction of market return for the following business day. The performance was maximized using Bayesian hyperparameter optimization and evaluated using nested cross-validation. The proof of concept was demonstrated using ten companies in the Dow Jones Index, which were grouped into five sectors. The findings indicate an asymmetric power of sentiment measures in different sectors, with the petroleum industry being the most responsive to the sentiment expressed in the news. The study highlights the significance of targeted sentiment measures in making informed decisions about the market direction, particularly for the petroleum industry.

关键词： Sentiment Analysis xgboost algorithm Hyperparameter Optimization Nested Cross-validation Stock Markets

来源：评论

学校读者我要写书评

暂无评论

Method for predicting the remaining mileage of electric vehicles based on dimension expansion and model fusion

引用

IET INTELLIGENT TRANSPORT SYSTEMS 2022年第8期16卷 1074-1091页

作者： Tian, Sheng Li, Chengwei Lv, Qing Li, Jia South China Univ Technol Sch Civil Engn & Transportat Guangzhou Peoples R China

Accurately predicting the remaining mileage of electric vehicles (EVs) can effectively alleviate user's mileage anxiety and develop refinement of energy management strategy. However, traditional prediction methods not only consume time and resources, but also accumulate errors and lack interpretability. In this paper, we proposed a model based on dimension expansion and model fusion strategy, which uses the extreme gradient boosting (xgboost) algorithm to directly predict the remaining mileage of EVs. After pre-processing the real running data of EVs, we constructed the field of remaining driving range and analyzed the relationship between features and remaining driving range, and then directly predicted the remaining driving mileage. Compared with other machine learning methods, xgboost model has the highest accuracy. Then dimensional extended data set was obtained based on prior knowledge and symbol conversion, which improved the model performance. Finally, the model fusion strategy was adopted to further improve the generalization ability and stability of the model. The experimental results show that the Bootstrap aggregating (Bagging) fusion model has the highest predictive performance on the test set and outperformed other methods. The maximum RAE is not more than 3.5%, RMSE is less than 3km and MAE is about 2 km.

关键词： energy management RMSE electric vehicles RAE model fusion dimension expansion supervised learning data handling dimensional extended data set neural nets generalisation (artificial intelligence) machine learning predictive performance remaining driving range symbol conversion remaining driving mileage bagging user mileage anxiety bootstrap aggregating fusion model extreme gradient boosting prior knowledge remaining mileage prediction xgboost algorithm generalization ability energy management systems traffic engineering computing mean square error methods

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：