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Добірка наукової літератури з теми "XGBOOST MODEL"

Автор: Grafiati

Опубліковано: 16 січня 2024

Оновлено: 12 червня 2025

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Статті в журналах з теми "XGBOOST MODEL"

Yang, Hao, Jiaxi Li, Siru Liu, Xiaoling Yang, and Jialin Liu. "Predicting Risk of Hypoglycemia in Patients With Type 2 Diabetes by Electronic Health Record–Based Machine Learning: Development and Validation." JMIR Medical Informatics 10, no. 6 (2022): e36958. http://dx.doi.org/10.2196/36958.

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Анотація:

Background Hypoglycemia is a common adverse event in the treatment of diabetes. To efficiently cope with hypoglycemia, effective hypoglycemia prediction models need to be developed. Objective The aim of this study was to develop and validate machine learning models to predict the risk of hypoglycemia in adult patients with type 2 diabetes. Methods We used the electronic health records of all adult patients with type 2 diabetes admitted to West China Hospital between November 2019 and December 2021. The prediction model was developed based on XGBoost and natural language processing. F1 score, area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA) were used as the main criteria to evaluate model performance. Results We included 29,843 patients with type 2 diabetes, of whom 2804 patients (9.4%) developed hypoglycemia. In this study, the embedding machine learning model (XGBoost3) showed the best performance among all the models. The AUC and the accuracy of XGBoost are 0.82 and 0.93, respectively. The XGboost3 was also superior to other models in DCA. Conclusions The Paragraph Vector–Distributed Memory model can effectively extract features and improve the performance of the XGBoost model, which can then effectively predict hypoglycemia in patients with type 2 diabetes.

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OUKHOUYA, HASSAN, HAMZA KADIRI, KHALID EL HIMDI, and RABY GUERBAZ. "Forecasting International Stock Market Trends: XGBoost, LSTM, LSTM-XGBoost, and Backtesting XGBoost Models." Statistics, Optimization & Information Computing 12, no. 1 (2023): 200–209. http://dx.doi.org/10.19139/soic-2310-5070-1822.

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   Forecasting time series is crucial for financial research and decision-making in business. The nonlinearity of stock market prices profoundly impacts global economic and financial sectors. This study focuses on modeling and forecasting the daily prices of key stock indices - MASI, CAC 40, DAX, FTSE 250, NASDAQ, and HKEX, representing the Moroccan, French, German, British, US, and Hong Kong markets, respectively. We compare the performance of machine learning models, including Long Short-Term Memory (LSTM), eXtreme Gradient Boosting (XGBoost), and the hybrid LSTM-XGBoost, and utilize the skforecast library for backtesting. Results show that the hybrid LSTM-XGBoost model, optimized using Grid Search (GS), outperforms other models, achieving high accuracy in forecasting daily prices. This contribution offers financial analysts and investors valuable insights, facilitating informed decision-making through precise forecasts of international stock prices.   

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Gu, Kai, Jianqi Wang, Hong Qian, and Xiaoyan Su. "Study on Intelligent Diagnosis of Rotor Fault Causes with the PSO-XGBoost Algorithm." Mathematical Problems in Engineering 2021 (April 26, 2021): 1–17. http://dx.doi.org/10.1155/2021/9963146.

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Анотація:

On basis of fault categories detection, the diagnosis of rotor fault causes is proposed, which has great contributions to the field of intelligent operation and maintenance. To improve the diagnostic accuracy and practical efficiency, a hybrid model based on the particle swarm optimization-extreme gradient boosting algorithm, namely, PSO-XGBoost is designed. XGBoost is used as a classifier to diagnose rotor fault causes, having good performance due to the second-order Taylor expansion and the explicit regularization term. PSO is used to automatically optimize the process of adjusting the XGBoost’s parameters, which overcomes the shortcomings when using the empirical method or the trial-and-error method to adjust parameters of the XGBoost model. The hybrid model combines the advantages of the two algorithms and can diagnose nine rotor fault causes accurately. Following diagnostic results, maintenance measures referring to the corresponding knowledge base are provided intelligently. Finally, the proposed PSO-XGBoost model is compared with five state-of-the-art intelligent classification methods. The experimental results demonstrate that the proposed method has higher diagnostic accuracy and practical efficiency in diagnosing rotor fault causes.

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Liu, Jialin, Jinfa Wu, Siru Liu, Mengdie Li, Kunchang Hu, and Ke Li. "Predicting mortality of patients with acute kidney injury in the ICU using XGBoost model." PLOS ONE 16, no. 2 (2021): e0246306. http://dx.doi.org/10.1371/journal.pone.0246306.

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Анотація:

Purpose The goal of this study is to construct a mortality prediction model using the XGBoot (eXtreme Gradient Boosting) decision tree model for AKI (acute kidney injury) patients in the ICU (intensive care unit), and to compare its performance with that of three other machine learning models. Methods We used the eICU Collaborative Research Database (eICU-CRD) for model development and performance comparison. The prediction performance of the XGBoot model was compared with the other three machine learning models. These models included LR (logistic regression), SVM (support vector machines), and RF (random forest). In the model comparison, the AUROC (area under receiver operating curve), accuracy, precision, recall, and F1 score were used to evaluate the predictive performance of each model. Results A total of 7548 AKI patients were analyzed in this study. The overall in-hospital mortality of AKI patients was 16.35%. The best performing algorithm in this study was XGBoost with the highest AUROC (0.796, p < 0.01), F1(0.922, p < 0.01) and accuracy (0.860). The precision (0.860) and recall (0.994) of the XGBoost model rank second among the four models. Conclusion XGBoot model had obvious advantages of performance compared to the other machine learning models. This will be helpful for risk identification and early intervention for AKI patients at risk of death.

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Ji, Shouwen, Xiaojing Wang, Wenpeng Zhao, and Dong Guo. "An Application of a Three-Stage XGBoost-Based Model to Sales Forecasting of a Cross-Border E-Commerce Enterprise." Mathematical Problems in Engineering 2019 (September 16, 2019): 1–15. http://dx.doi.org/10.1155/2019/8503252.

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Sales forecasting is even more vital for supply chain management in e-commerce with a huge amount of transaction data generated every minute. In order to enhance the logistics service experience of customers and optimize inventory management, e-commerce enterprises focus more on improving the accuracy of sales prediction with machine learning algorithms. In this study, a C-A-XGBoost forecasting model is proposed taking sales features of commodities and tendency of data series into account, based on the XGBoost model. A C-XGBoost model is first established to forecast for each cluster of the resulting clusters based on two-step clustering algorithm, incorporating sales features into the C-XGBoost model as influencing factors of forecasting. Secondly, an A-XGBoost model is used to forecast the tendency with the ARIMA model for the linear part and the XGBoost model for the nonlinear part. The final results are summed by assigning weights to forecasting results of the C-XGBoost and A-XGBoost models. By comparison with the ARIMA, XGBoost, C-XGBoost, and A-XGBoost models using data from Jollychic cross-border e-commerce platform, the C-A-XGBoost is proved to outperform than other four models.

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Zhu, Yiming. "Stock Price Prediction based on LSTM and XGBoost Combination Model." Transactions on Computer Science and Intelligent Systems Research 1 (October 12, 2023): 94–109. http://dx.doi.org/10.62051/z6dere47.

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Анотація:

In recent years, many machine learning and deep learning algorithms have been applied to stock prediction, providing a reference basis for stock trading, and LSTM neural network and XGBoost algorithm are two typical representatives, each with advantages and disadvantages in prediction. In view of this, we propose a combination model based on LSTM and XGBoost, which combines the advantages of LSTM in processing time series data and the ability of XGBoost to evaluate the importance of features. The combination model first selects feature variables with high importance through XGBoost, performs data dimensionality reduction, and then uses LSTM to make predictions. In order to verify the feasibility of the combination model, we built XGBoost, LSTM and LSTM-XGBoost models, and carried out experiments on three data sets of China Eastern Airlines, China Merchants Bank and Kweichow Moutai respectively. Finally, we concluded that the proposed LSTM-XGBoost model has good feasibility and universality in stock price prediction by comparing the accuracy of the predicted images and their performance in RMSE, RMAE, and MAPE indicators.

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Xiong, Shuai, Zhixiang Liu, Chendi Min, Ying Shi, Shuangxia Zhang, and Weijun Liu. "Compressive Strength Prediction of Cemented Backfill Containing Phosphate Tailings Using Extreme Gradient Boosting Optimized by Whale Optimization Algorithm." Materials 16, no. 1 (2022): 308. http://dx.doi.org/10.3390/ma16010308.

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Unconfined compressive strength (UCS) is the most significant mechanical index for cemented backfill, and it is mainly determined by traditional mechanical tests. This study optimized the extreme gradient boosting (XGBoost) model by utilizing the whale optimization algorithm (WOA) to construct a hybrid model for the UCS prediction of cemented backfill. The PT proportion, the OPC proportion, the FA proportion, the solid concentration, and the curing age were selected as input variables, and the UCS of the cemented PT backfill was selected as the output variable. The original XGBoost model, the XGBoost model optimized by particle swarm optimization (PSO-XGBoost), and the decision tree (DT) model were also constructed for comparison with the WOA-XGBoost model. The results showed that the values of the root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE) obtained from the WOA-XGBoost model, XGBoost model, PSO-XGBoost model, and DT model were equal to (0.241, 0.967, 0.184), (0.426, 0.917, 0.336), (0.316, 0.943, 0.258), and (0.464, 0.852, 0.357), respectively. The results show that the proposed WOA-XGBoost has better prediction accuracy than the other machine learning models, confirming the ability of the WOA to enhance XGBoost in cemented PT backfill strength prediction. The WOA-XGBoost model could be a fast and accurate method for the UCS prediction of cemented PT backfill.

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Wang, Yu, Li Guo, Yanrui Zhang, and Xinyue Ma. "Research on CSI 300 Stock Index Price Prediction Based On EMD-XGBoost." Frontiers in Computing and Intelligent Systems 3, no. 1 (2023): 72–77. http://dx.doi.org/10.54097/fcis.v3i1.6027.

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The combination of artificial intelligence techniques and quantitative investment has given birth to various types of price prediction models based on machine learning algorithms. In this study, we verify the applicability of machine learning fused with statistical method models through the EMD-XGBoost model for stock price prediction. In the modeling process, specific solutions are proposed for overfitting problems that arise. The stock prediction model of machine learning fused with statistical learning was constructed from an empirical perspective, and an XGBoost algorithm model based on empirical modal decomposition was proposed. The data set selected for the experiment was the closing price of the CSI 300 index, and the model was judged by four indicators:mean absolute error, mean error, and root mean square error, etc. The method used for the experiment was the EMD-XGBoost network model, which had the following advantages: first, combining the empirical modal decomposition method with the XGBoost model is conducive to mining the time series data for Second, the decomposition of the CSI 300 index data by the empirical modal decomposition method is helpful to improve the accuracy of the XGBoost model for time series data prediction. The experiments show that the EMD-XGBoost model outperforms the single ARIMA or LSTM network model as well as the EMD-LSTM network model in terms of mean absolute error, mean error, and root mean square error.

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Harriz, Muhammad Alfathan, Nurhaliza Vania Akbariani, Harlis Setiyowati, and Handri Santoso. "Enhancing the Efficiency of Jakarta's Mass Rapid Transit System with XGBoost Algorithm for Passenger Prediction." Jambura Journal of Informatics 5, no. 1 (2023): 1–6. http://dx.doi.org/10.37905/jji.v5i1.18814.

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This study is based on a machine learning algorithm known as XGBoost. We used the XGBoost algorithm to forecast the capacity of Jakarta's mass transit system. Using preprocessed raw data obtained from the Jakarta Open Data website for the period 2020-2021 as a training medium, we achieved a mean absolute percentage error of 69. However, after the model was fine-tuned, the MAPE was significantly reduced by 28.99% to 49.97. The XGBoost algorithm was found to be effective in detecting patterns and trends in the data, which can be used to improve routes and plan future studies by providing valuable insights. It is possible that additional data points, such as holidays and weather conditions, will further enhance the accuracy of the model in future research. As a result of implementing XGBoost, Jakarta's transportation system can optimize resource utilization and improve customer service in order to improve passenger satisfaction. Future studies may benefit from additional data points, such as holidays and weather conditions, in order to improve XGBoost's efficiency.

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Siringoringo, Rimbun, Resianta Perangin-angin, and Jamaluddin Jamaluddin. "MODEL HIBRID GENETIC-XGBOOST DAN PRINCIPAL COMPONENT ANALYSIS PADA SEGMENTASI DAN PERAMALAN PASAR." METHOMIKA Jurnal Manajemen Informatika dan Komputerisasi Akuntansi 5, no. 2 (2021): 97–103. http://dx.doi.org/10.46880/jmika.vol5no2.pp97-103.

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Анотація:

Extreme Gradient Boosting(XGBoost) is a popular boosting algorithm based on decision trees. XGBoost is the best in the boosting group. XGBoost has excellent convergence. On the other hand, XGBoost is a Hyper parameterized model. Determining the value of each parameter is classified as difficult, resulting in the results obtained being trapped in the local optimum situation. Determining the value of each parameter manually, of course, takes a lot of time. In this study, a Genetic Algorithm (GA) is applied to find the optimal value of the XGBoost hyperparameter on the market segmentation problem. The evaluation of the model is based on the ROC curve. Test result. The ROC test results for several SVM, Logistic Regression, and Genetic-XGBoost models are 0.89; 0.98; 0.99. The results show that the Genetic-XGBoost model can be applied to market segmentation and forecasting.

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