检索结果-内蒙古大学图书馆

xgboost algorithm under Differential Privacy Protection

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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Research on orthopedic auxiliary classification and prediction model based on xgboost algorithm

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NEURAL COMPUTING & APPLICATIONS 2020年第7期32卷 1971-1979页

作者： Li, Shenglong Zhang, Xiaojing China Med Univ Liaoning Canc Hosp & Inst Canc Hosp Dept Bone & Soft Tissue Tumor Surg Shenyang Liaoning Peoples R China

In the big data environment, hospital medical data are also becoming more complex and diversified. The traditional method of manually processing data has not been able to meet the management needs of massive medical data. With the further development of big data technology and machine learning, the smart medical aided diagnosis model came into being. However, there is almost no auxiliary diagnosis mode for orthopedic diseases. In order to make up for the gap in the auxiliary diagnosis of orthopedics and promote the wisdom process of orthopedic disease diagnosis, this paper proposes an orthopedic auxiliary classification prediction model based on xgboost algorithm. The experimental data were obtained from the clinical case information of femoral neck patients from April 2016 to October 2018, Department of Bone and Soft Tissue Tumor Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute. In order to make the experimental results more convincing, while constructing the xgboost model, the orthopedic auxiliary classification prediction model is constructed based on the random forest algorithm and the associated classification algorithm, respectively, and the three models are compared and analyzed. The results show that compared with the random forest model and the associated classification model, the xgboost algorithm classification prediction model has higher accuracy, faster calculation speed, and more applicability in orthopedic clinical data. The xgboost algorithm can cope with complex and diverse medical data, and can better meet the requirements of timeliness and accuracy of auxiliary diagnosis. The classification and prediction model of orthopedic auxiliary diagnosis proposed in this paper helps to reduce the workload of medical workers, help patients prevent and recover early, and realize real auxiliary medical services.

关键词： Orthopedic diseases Assisted diagnosis Classification prediction xgboost algorithm

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TS_XGB:Ultra-Short-Term Wind Power Forecasting Method Based on Fusion of Time-Spatial Data and xgboost algorithm

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Procedia Computer Science 2022年 199卷 1103-1111页

作者： Jiang Jiading Wang Feng Tang Rui Zhang Lingling Xu Xin Xinjiang Institute of Engineering Urumqi 830023 China Kunming University of Science and Technology Kunming 650093 China University of Chinese Academy of Sciences Beijing 100190 China The Hong Kong Polytechnic University Hong Kong China

Ultra-short-term wind power forecasting is of great significance to the safety, stableness, and optimal allocation of the power system. Aiming at the actual needs of increasing accuracy of wind power forecasting, this paper proposes an integrated learning algorithm based on time and spatial data mining according to the change rate of wind speed and the influence of wind direction, which is used in the ultra-short-term wind power forecasting model: TS_XGB. After calculating with the time series data of Xinjiang Datang Ruoqiang Wind Farm in 2018, the TS_XGB algorithm proposed in this paper has better RMSE and MAE performance than Linear Regression, Ridge Regression, SVM and Decision Tree in the 10-fold cross-validation test. The prediction effect shows that the TS_XGB model proposed in this paper can be effectively used for ultra-short-term wind power forecasting.

关键词： Time-Space data Feature fusion xgboost algorithm Wind power prediction

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Design of High Temperature Complex Dielectric Properties Measuring System Based on xgboost algorithm

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MATERIALS 2020年第6期13卷 1419页

作者： Wu, Yuanyuan Wu, Li Zhu, Huacheng Hong, Tao Chengdu Univ Technol Coll Informat Sci & Technol Chengdu 610000 Peoples R China Sichuan Univ Coll Elect & Informat Engn Chengdu 610065 Peoples R China China West Normal Univ Sch Elect Informat Engn Nanchong 637002 Peoples R China

This paper aims to propose an online relative complex permittivity measurement system at high temperature based on microwave interferometer. A ridge waveguide with a TE10 mode was used in which the sample was heated and measured simultaneously at a frequency of 2450 MHz, and the microwave interferometer is used to collect the amplitude and phase difference of the incident signal. The Extreme Gradient Boosting (xgboost) algorithm trained by the corresponding simulation data is used to construct the inversion model to calculate the complex dielectric coefficient of the tested material. Besides, this paper uses linear regression algorithm (LR) to calibrate the measurement system in order to improve the measurement accuracy. The entire system was tested using different materials at room temperature, and the maximum error of the measurement accuracy is less than 8% compared to the theoretical data. The robustness of the entire system was also tested by measuring Macor materials up to 800 degrees C. This proposed method provides an effective way to understand the mechanism between microwaves and matter at high temperatures.

关键词： dielectric property microwave interferometer xgboost algorithm

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Study on offshore seabed sediment classification based on particle size parameters using xgboost algorithm

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COMPUTERS & GEOSCIENCES 2021年 149卷 104713-104713页

作者： Wang, Fengfan Yu, Jia Liu, Zhijie Kong, Min Wu, Yunfan Natl Marine Data & Informat Serv 93 Liuwei Rd Tianjin 300171 Peoples R China Naval Inst Hydrog Surveying & Charting Tianjin 300061 Peoples R China

Folk's textual classification scheme which is widely used for sediment study operates with the proportions of gravel, sand, silt and clay fractions conventionally. However, dealing with data from different sources usually needs to face missing values that may make the classification difficult. To solve this problem and discover other methods of analyzing the scheme, with samples of offshore seabed sediment, a two-stage model was established to predict a sample's class using the xgboost algorithm as well as the grain size parameters as input features. The final model was evaluated with quantitative performance measures of recall, precision and F1 score, and by comparing sediment texture maps using the predicted and the actual data. The results show that the model performs well on extraction of sediment samples without gravel fraction, and prediction of classes that have independent characteristics of grain size parameters or samples not near the boundaries of classes in the ternary diagram. The predicted sediment texture is close to the actual and could be reliable due to errors with little impact on further applications. It is demonstrated that the model could be an auxiliary or alternative approach to offshore sediment texture mapping, as well as supplementary to the analysis of sedimentary environment.

关键词： Offshore seabed sediment texture xgboost algorithm Grain size parameters Folk's textural classification

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LTE Network Quality Analysis Method Based on MR Data and xgboost algorithm 5

LTE Network Quality Analysis Method Based on MR Data and XGB...

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5th IEEE International Conference on Big Data Analytics (ICBDA)

作者： Wang, Xi Zhou, Qiang China Mobile Commun Grp Fujian Co Wireless Network Optimizat Ctr Fuzhou Peoples R China China Mobile Grp Design Inst Co Ltd Dept Network Optimizat R&D Fujian Branch Fuzhou Peoples R China

ISBN: (纸本)9781728141114

At present, LTE network quality analysis is mainly based on Drive Test (DT) and Call Quality Test (CQT). Both methods require a large number of professional network optimization engineers and testing equipment. The testing methods cost a lot of time and money, and they cannot diagnose the causes of LTE network quality difference in time, which seriously affects customer perception. In this paper, a method of LTE network quality analysis based on Measurement Report (MR) data and xgboost classification algorithm is proposed to quickly diagnose the cause of poor quality network. This method can effectively and comprehensively reflect customer perception. It can provide useful information for network optimization. It greatly improves the efficiency of network optimization engineers, and reduces a large number of labor costs.

关键词： LTE network MR data classification xgboost algorithm

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A Remote Health Diagnosis Method Based on Full Voting xgboost algorithm

A Remote Health Diagnosis Method Based on Full Voting XGBoos...

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作者： Yuting Li Yang Yang Peng Yu Ying Yao Yong Yan State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications State Grid Zhejiang Electric Power Company Electric Power Research Institute

At present, classification models are widely used in medical disease monitoring. In remote health diagnosis technology, classification algorithm based on machine learning also plays a key role. Among them, xgboost model is a widely used classification model. However, due to the imbalance of disease detection data, it will lead to a series of problems such as insufficient model learning and poor classification effect. Researchers have proposed some data enhancement methods to solve this kind of problem, but they often do not perform well in polyphenol tasks. In order to solve this problem, this paper proposes a full vote algorithm based on xgboost model. For small sample data, a special dataset is generated and multiple models are trained. When classifying, all models vote in full to determine the classification result. Finally, we verify that our method performs better in the classification task of small sample disease detection data in simulation experiments.

关键词： Disease diagnosis Imbalanced data Machine learning xgboost algorithm Classification

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Prediction of weld bead geometry of MAG welding based on xgboost algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2019年第9-12期101卷 2283-2295页

作者： Chen, Kai Chen, Huabin Liu, Liang Chen, Shanben Shanghai Jiao Tong Univ Sch Mat Sci & Engn Shanghai Key Lab Mat Laser Proc & Modificat Shanghai 200240 Peoples R China

Evaluating the welding joint quality in real time is difficult for chassis parts robotic gas-shielded welding. Series of metal active gas (MAG) joints were conducted in this paper to investigate the relationship between welding current, welding speed, energy input, and weld bead geometry. Bead width and bead reinforcement are obtained using a line-structured light measurement method, and the penetration depth of the bead is measured with the macroscopic metallurgical microscope. The ratio of penetration depth to the plate thickness and reinforcement is chosen as the evaluation criterion of the joint quality. Based on xgboost algorithm, two data-driven models are proposed to recognize penetration status and predict the bead reinforcement. In the prediction results, the absolute error of the penetration coefficient is 0.079 at the maximum, and the average relative error is 11.06%. For the test result of reinforcement prediction model, the relative error is 20.5% on average. The test results show that the xgboost-based models can be used for real-time prediction of welding quality.

关键词： MAG welding xgboost algorithm line-structured light measurement penetration coefficient

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An xgboost algorithm for Predicting Purchasing Behaviour on E-Commerce Platforms

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2020年第5期27卷 1467-1471页

作者： Song, Peiyi Liu, Yutong Commun Univ China Sch Econ & Management Beijing 100024 Peoples R China

To improve and enhance the predictive ability of consumer purchasing behaviours on e-commerce platforms, a new method of predicting purchasing behaviour on e-commerce platforms is created in this paper. This study introduced the basic principles of the xgboost algorithm, analysed the historical data of an e-commerce platform, pre-processed the original data and constructed an e-commerce platform consumer purchase prediction model based on the xgboost algorithm. By using the traditional random forest algorithm for comparative analysis, the K-fold cross-validation method was further used, combined with model performance indicators such as accuracy rate, precision rate, recall rate and F-1-score to evaluate the classification accuracy of the model. The characteristics of the importance of the results were found through visual analysis. The results indicated that using the xgboost algorithm to predict the purchasing behaviours of e-commerce platform consumers can improve the performance of the method and obtain a better prediction effect. This study provides a reference for improving the accuracy of e-commerce platform consumers' purchasing behaviours prediction, and has important practical significance for the efficient operation of e-commerce platforms.

关键词： e-commerce platform purchasing behaviour prediction xgboost algorithm

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Research on Pavement Skid Resistance Performance Prediction Model Based on Big Data Analysis and xgboost algorithm 5

Research on Pavement Skid Resistance Performance Prediction ...

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5th International Conference on Fuzzy Systems and Data Mining (FSDM) held in conjunction with the 9th International Conference on Electronics, Communications and Networks (CECNet)

作者： Li, Sili Shang, Qianli Tian, Bo Minist Transport Res Inst Highway 8 Xitucheng Rd Beijing Peoples R China

ISBN: (纸本)9781643680194;9781643680187

In order to explore the correlation among various influence factors on pavement skid resistance performance and improve the performance prediction accuracy, this research has established a pavement skid resistance performance prediction model based on xgboost algorithm, with the analysis and pre-process of related big data from LTPP database. The research then uses this prediction model to analyze the influence of basic features and time sequence features on pavement's skid resistance performance, and compares the prediction results with other commonly used prediction models to evaluate the prediction accuracy and effectiveness. Results have shown that in terms of model evaluation index R2 and RMSE values, the xgboost model has much better prediction performance than the linear regression model LR, the gray model GM, and the BPNN model. According to the output of xgboost model, the initial side-way force coefficient is the most important indicator for predicting SFC, while rainfall and snowfall are also strongly correlated with skid resistance performance prediction. Meanwhile, if target characteristics and prediction features modified to different occasions, this xgboost prediction model has great potential for even wider application.

关键词： road engineering prediction model xgboost algorithm side-way force coefficient skid resistance performance big data

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