检索结果-内蒙古大学图书馆

Financial Fraud: Identifying Corporate Tax Report Fraud Under the xgboost algorithm

EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2023年第4期10卷 1-7页

作者： Li, Xianjuan Hunan City Univ Yiyang 413000 Hunan Peoples R China

INTRODUCTION: With the development of economy, the phenomenon of financial fraud has become more and more frequent. OBJECTIVES: This paper aims to study the identification of corporate tax report falsification. METHODS: Firstly, financial fraud was briefly introduced;then, samples were selected from CSMAR database, 18 indicators related to fraud were selected from corporate tax reports, and 13 indicators were retained after information screening;finally, the xgboost algorithm was used to recognize tax report falsification. RESULTS: The xgboost algorithm had the highest accuracy rate (94.55%) when identifying corporate tax statement falsification, and the accuracy of the other algorithms such as the Logistic regressive algorithm were below 90%;the F1 value of the xgboost algorithm was also high, reaching 90.1%;it also had the shortest running time (55 s). CONCLUSION: The results prove the reliability of the xgboost algorithm in the identification of corporate tax report falsification. It can be applied in practice.

关键词： financial fraud corporate tax falsification identification xgboost algorithm

来源：评论

学校读者我要写书评

暂无评论

Clinical Data based xgboost algorithm for infection risk prediction of patients with decompensated cirrhosis: a 10-year (2012-2021) Multicenter Retrospective Case-control study

引用

BMC GASTROENTEROLOGY 2023年第1期23卷 1-10页

作者： Zheng, Jing Li, Jianjun Zhang, Zhengyu Yu, Yue Tan, Juntao Liu, Yunyu Gong, Jun Wang, Tingting Wu, Xiaoxin Guo, Zihao Chongqing Med Univ Banan Hosp Operat Management Off Chongqing 401320 Peoples R China Chongqing Med Univ Banan Hosp Dept Cardiothorac Surg Chongqing 401320 Peoples R China Zhejiang Univ Affiliated Hosp 1 Sch Med Med Records Dept Hangzhou 310003 Peoples R China Mayo Clin Sr Bioinformatician Dept Quantitat Hlth Sci Rochester MN 55905 USA Chongqing Med Univ Affiliated Hosp 2 Med Records Dept Chongqing 400010 Peoples R China Chongqing Med Univ Univ Town Hosp Dept Informat Ctr Chongqing 401331 Peoples R China Chongqing Med Univ Coll Med Informat Chongqing 400016 Peoples R China Zhejiang Univ Sch Med Affiliated Hosp 1 Natl Clin Res Ctr Infect DisState Key Lab Diag & 79 Qing Chun Rd Hangzhou 310003 Zhejiang Peoples R China Chongqing Banan Canc Hosp Dept Gastroenterol Chongqing 400054 Peoples R China

Objectives To appraise effective predictors for infection in patients with decompensated cirrhosis (DC) by using xgboost algorithm in a retrospective case-control study. Methods Clinical data were retrospectively collected from 6,648 patients with DC admitted to five tertiary hospitals. Indicators with significant differences were determined by univariate analysis and least absolute contraction and selection operator (LASSO) regression. Further multi-tree extreme gradient boosting (xgboost) machine learning-based model was used to rank importance of features selected from LASSO and subsequently constructed infection risk prediction model with simple-tree xgboost model. Finally, the simple-tree xgboost model is compared with the traditional logical regression (LR) model. Performances of models were evaluated by area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. Results Six features, including total bilirubin, blood sodium, albumin, prothrombin activity, white blood cell count, and neutrophils to lymphocytes ratio were selected as predictors for infection in patients with DC. Simple-tree xgboost model conducted by these features can predict infection risk accurately with an AUROC of 0.971, sensitivity of 0.915, and specificity of 0.900 in training set. The performance of simple-tree xgboost model is better than that of traditional LR model in training set, internal verification set, and external feature set (P<0.001). Conclusions The simple-tree xgboost predictive model developed based on a minimal amount of clinical data available to DC patients with restricted medical resources could help primary healthcare practitioners promptly identify potential infection.

关键词： Decompensated cirrhosis Infection xgboost algorithm Prediction model Multicenter

来源：评论

学校读者我要写书评

暂无评论

Prediction of Self-rated Health of Older Adults by Network Services Based on Agent Simulation and xgboost algorithm 1

引用

13th International Conference on Computer Engineering and Networks (CENet)

作者： Li, Yue Hu, Xinyue Li, Yang Zhang, Chengmeng Chen, Gong Peking Univ Inst Populat Res Beijing 100871 Peoples R China

ISBN: (数字)9789819992478

ISBN: (纸本)9789819992492;9789819992478;9789819992461

As the aging population grows, health issues have attracted widespread attention, especially among older adults. We explored the effects and simulation prediction of network services based on a multi-dimensional analysis of the health status of older adults, which could help older adults to better manage and evaluate their health. The study was conducted as follows. Based on the China Family Panel Studies (CFPS) data, a chi-square test was used to screen out 16 indicators with significant effects on the self-rated health (SRH) of older adults. To eliminate selection bias between samples, a propensity score matching (PSM) was used to explore the potential impact of network services on SRH of older adults. A multi-agent simulation model was constructed to examine SRH effects and then compare the health both before and after using network services based on the AnyLogic platform. The xgboost algorithm was used to build a prediction model for assessing SRH of older adults. The experimental results show that network services have a positive effect on SRH of older adults using the multiple PSM methods, improving SRH of older adults by 14.1%. Meanwhile, the multi-agent simulation proves that network services can improve the health status of older adults. It also proves that the xgboost algorithm has better accuracy, specificity, and running time than the other compared algorithms, and can meet the prediction needs of this paper. This study may enrich and expand the theoretical framework of health influence mechanism and simulation prediction studies.

关键词： Self-rated health Agent simulation xgboost algorithm Network services

来源：评论

学校读者我要写书评

暂无评论

Robustness of xgboost algorithm to Missing Features for Binary Classification of Medical Data 23

Robustness of XGBoost Algorithm to Missing Features for Bina...

引用

23rd International Symposium INFOTEH-JAHORINA (INFOTEH)

作者： Stokanovic, Smilja Dukic, David Miljkovic, Nadica Univ Belgrade Sch Elect Engn Belgrade Serbia Univ Ljubljana Fac Elect Engn Ljubljana Slovenia

ISBN: (纸本)9798350329940;9798350307368

The ability of the Extreme Gradient Boosting (xgboost) algorithm to classify subjects with different type of breast cancer and those with and without heart disease is explored by artificially imputing fractions of missing values on two diverse medical datasets. Likewise, substitution of missing values by mean and median is considered, as well as influence of learning rate on xgboost model. Our results indicate that xgboost algorithm can handle missing data internally yielding slightly better or slightly worse results (<2.5%) when simple imputation is applied. Further, learning rate hyperparameter did not display a major influence on the classifier performance. Expectantly, xgboost reaches different evaluation scores for diverse datasets, while F1-Score is not severely affected by missing values: for 90% of missing instances it dropped to 94.1% and to 73% for breast cancer and heart failure data, respectively. We would argue that xgboost with lower learning rates may be a good choice for classification of medical data, especially in cases when missing values are inevitable.

关键词： learning rate machine learning medical data missing data imputation xgboost algorithm

来源：评论

学校读者我要写书评

暂无评论

Intelligent prediction of short-term load of distribution network based on xgboost algorithm 36

Intelligent prediction of short-term load of distribution ne...

引用

36th Chinese Control and Decision Conference (CCDC)

作者： Wang, Ziang Wang, Dazhi Li, Yanming Northeastern Univ Coll ofInformat Sci & Engn Shenyang Peoples R China China Northern Vehicle Res Inst Beijing Peoples R China

ISBN: (纸本)9798350387780;9798350387797

Accurate load forecasting is an important link in distribution network planning, which is of great significance to realize economical and comprehensive energy service and safe operation. If the load forecasting can he carried out correctly, the social and economic benefits of the power grid will he effectively improved. Power load data is affected by many factors, and its random variation is not only related to the production and living behavior of residents, but also restricted by many environmental conditions. The accuracy of load data of power system makes the load forecasting of power system more complicated, which makes the stable, safe and economical operation of power system more difficult. In this paper, the characteristics and basic principles of xgboost intelligent algorithm are described. On the basis of parameter performance optimization using xgboost algorithm, the actual historical power load of an industrial test park is taken as an example to carry out experimental analysis on the data set.

关键词： xgboost algorithm power system short-term load forecasting

来源：评论

学校读者我要写书评

暂无评论

An xgboost algorithm Based Missing Value Filling Method for Power Data 24

An XGBoost Algorithm Based Missing Value Filling Method for ...

引用

International Conference on Power Electronics and Artificial Intelligence (PEAI)

作者： Qu, Guiteng Wang, Zehua Wang, Ziheng Shenyang Agr Univ Shenyang Liaoning Peoples R China State Grid Jilin Elect Power Co Ltd Changchun Jilin Peoples R China Changchun Univ Technol Changchun Jilin Peoples R China

ISBN: (纸本)9798400716638

With the development of data science and computer technology, data processing has also become an essential part of our life, work, and research, however, the increase of data storage will inevitably lose some data. In this study, for the problem of missing electric power data, a data missing value filling method based on the xgboost (eXtreme Gradient Boosting) algorithm is proposed by utilizing Python language. The method predicts and fills the missing data by analyzing and processing the electric power data and using the xgboost algorithm. Firstly, the missing eigenvalues are divided according to the characteristic importance of the eigenvalues in the target sample set mainly based on electric power data, and then for the missing samples under each eigenvalue, the xgboost model is constructed for the prediction and filling of the missing values, and finally, the complete sample set is obtained. The results show that the data missing value filling method based on the xgboost algorithm can predict the missing values more accurately and improve the accuracy and efficiency of data filling.

关键词： Data processing Python xgboost algorithm Missing value filling Data analysis

来源：评论

学校读者我要写书评

暂无评论

Application of xgboost algorithm in the optimization of pollutant concentration

引用

ATMOSPHERIC RESEARCH 2022年 276卷

作者： Li, Jiangtao An, Xingqin Li, Qingyong Wang, Chao Yu, Haomin Zhou, Xinyuan Geng, Yangli-ao Chinese Acad Meteorol Sci State Key Lab Severe Weather & Key Lab Atmospher C Beijing 100081 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China

With the rapid development of the economy, urban pollution has become a hot issue of human concern, and people are put forward higher requirements for the accuracy of pollutant concentration simulations. Based on the the WRF-Chem model simulation results and Beijing environmental monitoring data, this study constructed the xgboost algorithm through the process of data cleaning, feature selection and super parameter optimization, and the optimized simulation of PM2.5 and O3 concentrations in Beijing was then carried out based on the constructed algorithm. The results showed that the xgboost algorithm can better capture the spatial and temporal variation patterns of pollutant concentrations, and has a greater improvement on the simulation results of the WRF-Chem model, and that the xgboost algorithm shows better optimisation results in urban areas compared to suburban areas. In addition, the overall analysis of the features of PM2.5 and O3 concentrations based on the SHAP value theory showed that the time series and periodic features, aerosol ion concentration of Sodium (NAAJ) and Nitrate concentration (NO3AJ) were the important features affecting the prediction of PM2.5 concentration by the xgboost algorithm, and the most important factor affecting O3 concentration is temperature. PM2.5 and O3 concentrations were divided into three levels, and several samples were selected for single sample analysis. The analysis showed that at low pollutant concentration, most of the features made negative contributions to the concentration prediction, while at high concentration, most of the features made positive contributions. The contribution values of different features varied greatly and were unevenly distributed. The results of prediction were basically composed of a few features with large feature contributions, and the feature contributions of the same feature to different concentration prediction results were also different. Moreover, the xgboost algorithm was used to opti

关键词： xgboost algorithm WRF-Chem model SHAP value Feature contributions O3 PM2 5

来源：评论

学校读者我要写书评

暂无评论

Vegetable Sales Forecasting Based on xgboost algorithm and Random Forest 5

Vegetable Sales Forecasting Based on XGBOOST Algorithm and R...

引用

5th International Conference on Computers, Information Processing and Advanced Education, CIPAE 2024

作者： Li, Jun Zhang, Jinyang Sun, Mengxin Zhu, Rongyu School of Electrical Engineering Automation Guilin University of Electronic Technology Guilin China School of Culture and Communication Guilin Tourism University Guilin China

ISBN: (纸本)9798331527662

This paper explores the forecasting of vegetable sales and the optimization of procurement strategies using the xgboost algorithm and random forest methodology. The study begins by utilizing a factor analysis model to investigate the comprehensive correlation of vegetable sales among various types. It then employs Spearman correlation analysis to evaluate the specific inter-relationships between different vegetable groups. The examination uncovers notable disparities in the aggregate sales quantities among vegetable categories, with the strongest association found between solanaceous and aquatic root vegetables. Afterward, regression models based on random forest and xgboost are constructed to forecast the sales quantities for distinct vegetable categories. The outcomes show that the xgboost regression model attains an R-squared value of 0.945, outperforming the random forest regression model, which has a R-squared value of 0.576. In addition, a time series model is created to predict the future wholesale pricing of individual vegetable categories, exhibiting a satisfactory model fit with an R-squared value of 0.619. © 2024 IEEE.

关键词： Factor analysis Random forest xgboost algorithm

来源：评论

学校读者我要写书评

暂无评论

Implementing Calorie Burnt Prediction Through xgboost algorithm Compared Over Random Forest Regressor 15

Implementing Calorie Burnt Prediction Through XGBOOST Algori...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Reddy, K. Hima Vamsi Fernandez, Terrance Frederick Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Dept of Cse Chennai India

ISBN: (纸本)9798350370249

This research aims to evaluate the xgboost algorithm and the Lasso Regression approach for their respective abilities to forecast caloric expenditure during physical activity. Tools and Techniques for Scientific Investigation: Information such as calorie burn rates from different activities are included into the planned research. The xgboost technique and state-of-the-art Lasso Regression were among the machine learning technologies used to ascertain these calories. The xgboost method employs Innovative Lasso Regression sampling with a 10-sample size and a 0.8 G power value. According to an independent sample T-test, the two methods' accuracy varies by 0.001 (p © 2024 IEEE.

关键词： Calorie Gender Machine Learning Novel Random Forest Regressor Prediction xgboost algorithm

来源：评论

学校读者我要写书评

暂无评论

A Study on Sailboat Price Prediction Method Based on Multiple Linear Regression Model and xgboost algorithm 6

A Study on Sailboat Price Prediction Method Based on Multipl...

引用

2023 IEEE 6th International Conference on Information Systems and Computer Aided Education, ICISCAE 2023

作者： Chen, Benteng Beijing University of Chemical Technology School of International Education Beijing China

ISBN: (纸本)9798350313444

this study aims to explore sailboat price prediction methods through the utilization of a multiple linear regression model and the xgboost algorithm, addressing the pricing challenges in the second-hand sailboat market. The flourishing global economy has led to widespread interest in the second-hand vessel market. This paper provides a dataset containing detailed information on approximately 3,500 sailboats, encompassing attributes such as brand, length, geographic region, and listing price. The model is built upon sound assumptions and constraints, ensuring the reliability of the outcomes. Data preprocessing involves merging, handling outliers, addressing duplicates, and reducing data dimensions. PCA and stepwise regression are employed for dimensionality reduction. The model employs both the multiple linear regression and xgboost algorithm to establish predictive models. Through a case study involving the Hong Kong market, the model's predictive performance is optimized. Results demonstrate that the model exhibits strong predictive capabilities for specific regional markets. Through the integration of the multiple linear regression model and the xgboost algorithm, this study presents a valuable sailboat price prediction approach, offering decision support for market practitioners and investors. The research outcomes hold practical applications and academic value in the context of second-hand sailboat transactions. © 2023 IEEE.

关键词： multiple linear regression model PCA Sailboat price prediction method stepwise regression xgboost algorithm

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：