Relevant bibliographies by topics / XGBOOST MODEL

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'XGBOOST MODEL'

Author: Grafiati
Published: 16 January 2024
Last updated: 25 July 2025

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Journal articles on the topic "XGBOOST MODEL"

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Zeng, Fanchao, Qing Gao, Lifeng Wu, et al. "Modeling Short-Term Drought for SPEI in Mainland China Using the XGBoost Model." Atmosphere 16, no. 4 (2025): 419. https://doi.org/10.3390/atmos16040419.

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Accurate drought prediction is crucial for optimizing water resource allocation, safeguarding agricultural productivity, and maintaining ecosystem stability. This study develops a methodological framework for short-term drought forecasting using SPEI time series (1979–2020) and evaluates three predictive models: (1) a baseline XGBoost model (XGBoost1), (2) a feature-optimized XGBoost variant incorporating Pearson correlation analysis (XGBoost2), and (3) an enhanced CPSO-XGBoost model integrating hybrid particle swarm optimization with dual mechanisms of binary feature selection and parameter t
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Zhao, Tianwen, Guoqing Chen, Sujitta Suraphee, Tossapol Phoophiwfa, and Piyapatr Busababodhin. "A hybrid TCN-XGBoost model for agricultural product market price forecasting." PLOS One 20, no. 5 (2025): e0322496. https://doi.org/10.1371/journal.pone.0322496.

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Price volatility in agricultural markets is influenced by seasonality, supply-demand fluctuations, policy changes, and climate. These factors significantly impact agricultural production and the broader macroeconomy. Traditional time series models, limited by linear assumptions, often fail to capture the nonlinear nature of price fluctuations. To address this limitation, we propose an integrated forecasting model that combines TCN and XGBoost to improve the accuracy of agricultural price volatility predictions. TCN captures both short-term and long-term dependencies using convolutional operati
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Nabilah Selayanti, Dwi Amalia Putri, Trimono Trimono, and Mohammad Idhom. "PREDIKSI HARGA PENUTUPAN SAHAM BBRI DENGAN MODEL HYBRID LSTM-XGBOOST." Informatika: Jurnal Teknik Informatika dan Multimedia 5, no. 1 (2025): 52–64. https://doi.org/10.51903/informatika.v5i1.1011.

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The ease of investing in the digital era has driven Generation Z to dominate stock market participation, particularly in blue-chip stocks such as PT Bank Rakyat Indonesia Tbk (BBRI). However, stock price fluctuations influenced by macroeconomic factors, regulations, and global market sentiment make it difficult for investors to make accurate decisions. Decisions based on insufficient information pose a significant risk of loss, especially for novice investors. This study proposes a hybrid LSTM-XGBoost approach for predicting BBRI stock prices, combining the strengths of LSTM in capturing nonli
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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, a
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Tran, Thanh-Ngoc, and Quoc-Dai Nguyen. "Research on the Influence of Genetic Algorithm Parameters on XGBoost in Load Forecasting." Engineering, Technology & Applied Science Research 14, no. 6 (2024): 18849–54. https://doi.org/10.48084/etasr.8863.

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Electric load forecasting is crucial in a power system comprising electricity generation, transmission, distribution, and retail. Due to its high accuracy, the ensemble learning method XGBoost has been widely applied in load forecasting. XGBoost's performance depends on its hyperparameters and the Genetic Algorithm (GA) is a commonly used algorithm in determining the optimal hyperparameters for this model. In this study, we propose a flowchart algorithm to investigate the impact of GA parameters on the accuracy of XGBoost models over the hyperparameter grid for load forecasting. The maximum lo
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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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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, a
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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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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 XGBoo
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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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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), an
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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 re
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Sovia, Nabila Ayunda, Ni Wayan Surya Wardhani, Eni Sumarminingsih, and Elvo Ramadhan Shofa. "Enhancing Image Classification of Cabbage Plant Diseases Using a Hybrid Model Convolutional Neural Network and XGBoost." CAUCHY: Jurnal Matematika Murni dan Aplikasi 10, no. 1 (2025): 278–89. https://doi.org/10.18860/cauchy.v10i1.30866.

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Classifying imbalanced datasets presents significant challenges, often leading to biased model performance, particularly in multiclass classification. This study addresses these issues by integrating Convolutional Neural Networks (CNN) and XGBoost, leveraging CNN’s exceptional feature extraction capabilities and XGBoost's robust handling of imbalanced data. The Hybrid CNN-XGBoost model was applied to classify cabbage plants affected by pests and diseases, which are categorized into five classes, with a significant imbalance between healthy and affected plants. The dataset, characterized by sev
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Dissertations / Theses on the topic "XGBOOST MODEL"

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Matos, Sara Madeira. "Interpretable models of loss given default." Master's thesis, Instituto Superior de Economia e Gestão, 2021. http://hdl.handle.net/10400.5/20981.

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Mestrado em Econometria Aplicada e Previsão<br>A gestão do risco de crédito é uma área em que os reguladores esperam que os bancos adotem modelos de risco transparentes e auditáveis colocando de parte o uso de modelos de black-box apesar destes serem mais precisos. Neste estudo, mostramos que os bancos não precisam de sacrificar a precisão preditiva ao custo da transparência do modelo para estar em conformidade com os requisitos regulatórios. Ilustramos isso mostrando que as previsões de perdas de crédito fornecidas por um modelo black-box podem ser facilmente explicadas em termos dos seus inp
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Wigren, Richard, and Filip Cornell. "Marketing Mix Modelling: A comparative study of statistical models." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160082.

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Deciding the optimal media advertisement spending is a complex issue that many companies today are facing. With the rise of new ways to market products, the choices can appear infinite. One methodical way to do this is to use Marketing Mix Modelling (MMM), in which statistical modelling is used to attribute sales to media spendings. However, many problems arise during the modelling. Modelling and mitigation of uncertainty, time-dependencies of sales, incorporation of expert information and interpretation of models are all issues that need to be addressed. This thesis aims to investigate the ef
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Pettersson, Gustav, and John Almqvist. "Lavinprognoser och maskininlärning : Att prediktera lavinprognoser med maskininlärning och väderdata." Thesis, Uppsala universitet, Institutionen för informatik och media, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-387205.

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Denna forskningsansats undersöker genomförbarheten i att prediktera lavinfara med hjälp av ma-skininlärning i form avXGBoostoch väderdata. Lavinprognoser och meterologisk vädermodelldata harsamlats in för de sex svenska fjällområden där Naturvårdsveket genomlavinprognoser.sepublicerar lavin-prognoser. Lavinprognoserna har hämtats frånlavinprognoser.seoch den vädermodelldata som användsär hämtad från prognosmodellen MESAN, som produceras och tillhandahålls av Sveriges meteorologiskaoch hydrologiska institut. 40 modeller av typenXGBoosthar sedan tränats på denna datamängd, medsyfte att predikter
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Karlsson, Henrik. "Uplift Modeling : Identifying Optimal Treatment Group Allocation and Whom to Contact to Maximize Return on Investment." Thesis, Linköpings universitet, Statistik och maskininlärning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157962.

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This report investigates the possibilities to model the causal effect of treatment within the insurance domain to increase return on investment of sales through telemarketing. In order to capture the causal effect, two or more subgroups are required where one group receives control treatment. Two different uplift models model the causal effect of treatment, Class Transformation Method, and Modeling Uplift Directly with Random Forests. Both methods are evaluated by the Qini curve and the Qini coefficient. To model the causal effect of treatment, the comparison with a control group is a necessit
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Henriksson, Erik, and Kristopher Werlinder. "Housing Price Prediction over Countrywide Data : A comparison of XGBoost and Random Forest regressor models." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302535.

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The aim of this research project is to investigate how an XGBoost regressor compares to a Random Forest regressor in terms of predictive performance of housing prices with the help of two data sets. The comparison considers training time, inference time and the three evaluation metrics R2, RMSE and MAPE. The data sets are described in detail together with background about the regressor models that are used. The method makes substantial data cleaning of the two data sets, it involves hyperparameter tuning to find optimal parameters and 5foldcrossvalidation in order to achieve good performance e
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Kinnander, Mathias. "Predicting profitability of new customers using gradient boosting tree models : Evaluating the predictive capabilities of the XGBoost, LightGBM and CatBoost algorithms." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-19171.

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In the context of providing credit online to customers in retail shops, the provider must perform risk assessments quickly and often based on scarce historical data. This can be achieved by automating the process with Machine Learning algorithms. Gradient Boosting Tree algorithms have demonstrated to be capable in a wide range of application scenarios. However, they are yet to be implemented for predicting the profitability of new customers based solely on the customers’ first purchases. This study aims to evaluate the predictive performance of the XGBoost, LightGBM, and CatBoost algorithms in
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Svensson, William. "CAN STATISTICAL MODELS BEAT BENCHMARK PREDICTIONS BASED ON RANKINGS IN TENNIS?" Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447384.

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The aim of this thesis is to beat a benchmark prediction of 64.58 percent based on player rankings on the ATP tour in tennis. That means that the player with the best rank in a tennis match is deemed as the winner. Three statistical model are used, logistic regression, random forest and XGBoost. The data are over a period between the years 2000-2010 and has over 60 000 observations with 49 variables each. After the data was prepared, new variables were created and the difference between the two players in hand taken all three statistical models did outperform the benchmark prediction. All thre
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Liu, Xiaoyang. "Machine Learning Models in Fullerene/Metallofullerene Chromatography Studies." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/93737.

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Machine learning methods are now extensively applied in various scientific research areas to make models. Unlike regular models, machine learning based models use a data-driven approach. Machine learning algorithms can learn knowledge that are hard to be recognized, from available data. The data-driven approaches enhance the role of algorithms and computers and then accelerate the computation using alternative views. In this thesis, we explore the possibility of applying machine learning models in the prediction of chromatographic retention behaviors. Chromatographic separation is a key techni
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Sharma, Vibhor. "Early Stratification of Gestational Diabetes Mellitus (GDM) by building and evaluating machine learning models." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281398.

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Gestational diabetes Mellitus (GDM), a condition involving abnormal levels of glucose in the blood plasma has seen a rapid surge amongst the gestating mothers belonging to different regions and ethnicities around the world. Cur- rent method of screening and diagnosing GDM is restricted to Oral Glucose Tolerance Test (OGTT). With the advent of machine learning algorithms, the healthcare has seen a surge of machine learning methods for disease diag- nosis which are increasingly being employed in a clinical setup. Yet in the area of GDM, there has not been wide spread utilization of these algorit
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Gregório, Rafael Leite. "Modelo híbrido de avaliação de risco de crédito para corporações brasileiras com base em algoritmos de aprendizado de máquina." Universidade Católica de Brasília, 2018. https://bdtd.ucb.br:8443/jspui/handle/tede/2432.

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Submitted by Sara Ribeiro (sara.ribeiro@ucb.br) on 2018-08-08T13:33:03Z No. of bitstreams: 1 RafaelLeiteGregorioDissertacao2018.pdf: 1382550 bytes, checksum: 9c6e4f1d3c561482546aca581262b92b (MD5)<br>Approved for entry into archive by Sara Ribeiro (sara.ribeiro@ucb.br) on 2018-08-08T13:33:24Z (GMT) No. of bitstreams: 1 RafaelLeiteGregorioDissertacao2018.pdf: 1382550 bytes, checksum: 9c6e4f1d3c561482546aca581262b92b (MD5)<br>Made available in DSpace on 2018-08-08T13:33:24Z (GMT). No. of bitstreams: 1 RafaelLeiteGregorioDissertacao2018.pdf: 1382550 bytes, checksum: 9c6e4f1d3c561482546aca581262b9
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Books on the topic "XGBOOST MODEL"

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Nokeri, Tshepo Chris. Data Science Solutions with Python: Fast and Scalable Models Using Keras, Pyspark MLlib, H2O, XGBoost, and Scikit-Learn. Apress L. P., 2022.

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Book chapters on the topic "XGBOOST MODEL"

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Saadat, Sumaya, and V. Joseph Raymond. "Malware Classification Using CNN-XGBoost Model." In Artificial Intelligence Techniques for Advanced Computing Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5329-5_19.

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Venkat, Karthik, Tarika Gautam, Mohit Yadav, and Mukhtiar Singh. "An XGBoost Ensemble Model for Residential Load Forecasting." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8443-5_26.

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Zhao, Yang, Yi Li, and Pengle Cheng. "XGBoost Lane-Changing Decision Model Considering Driving Style." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3052-0_21.

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Zhong, Weijian, Xiaoqin Lian, Chao Gao, Xiang Chen, and Hongzhou Tan. "PM2.5 Concentration Prediction Based on mRMR-XGBoost Model." In Machine Learning and Intelligent Communications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04409-0_30.

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Ren, Xudie, Haonan Guo, Shenghong Li, Shilin Wang, and Jianhua Li. "A Novel Image Classification Method with CNN-XGBoost Model." In Digital Forensics and Watermarking. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64185-0_28.

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Huang, Yan-Long, and JainShing Wu. "Predicting Twitter Posts from Fake Accounts Using XGBoost Model." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4182-3_28.

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Ye, Lu. "Credit Rating of Chinese Companies Based on XGBoost Model." In New Perspectives and Paradigms in Applied Economics and Business. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-23844-4_8.

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Chang, Wen-Chih, Yi-Hong Guo, Ya-Ling Yang, et al. "Using the XGBoost Model to Predict Santander Customer Trading." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0115-6_11.

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Shaban, Mohammad, Prerna Jain, and Ashish Prajesh. "Bayesian Optimization with XGBoost Model for Solar Irradiance Forecasting." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-0824-9_12.

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Bai, Yunjie, Xuezhi Wu, and Aimin Yang. "Improved XGBoost-MLP Model and Application to Performance Prediction." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4207-6_38.

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Conference papers on the topic "XGBOOST MODEL"

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Andotra, Mansi, and Ramesh Kumar Sunkaria. "Early Detection of Arrhythmia Using XGBoost Model." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724550.

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Rai, Rajnish, and Veena Thenkanidiyoor. "Detection of Specific Language Impairment Using XGBoost Model." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725529.

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Pang, Yanzhi, Xiang Wang, Yang Tang, et al. "A traffic accident early warning model based on XGBOOST." In Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), edited by Qinghua Lu and Weishan Zhang. SPIE, 2024. http://dx.doi.org/10.1117/12.3049832.

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Luo, Weiqi. "Prediction of Flight Delays Based on the XGboost Model." In 2024 International Conference on Computers, Information Processing and Advanced Education (CIPAE). IEEE, 2024. https://doi.org/10.1109/cipae64326.2024.00024.

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Nimmala, Satyanarayana, Maragoni Mahendar, Pinnapureddy Manasa, H. N. Lakshmi, Medikonda Asha Kiran, and C. Raghavendra. "Fuzzy-Enhanced XGBoost Model for Classifying Kidney Disease Severity." In 2024 4th International Conference on Soft Computing for Security Applications (ICSCSA). IEEE, 2024. https://doi.org/10.1109/icscsa64454.2024.00011.

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Li, Hang, Kaikai Liu, and Peize Lu. "A clinical diagnosis prediction model based on XGBoost-SHAP." In 2024 IEEE 2nd International Conference on Electrical, Automation and Computer Engineering (ICEACE). IEEE, 2024. https://doi.org/10.1109/iceace63551.2024.10898650.

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ling, Xuedong, Dachuang Li, Chidong Xu, Zhanying Zhang, Li Zhang, and KaiKun Niu. "Fast XGBoost model-based intracranial hemorrhage research on detection." In Second International Conference on Optoelectronic Information and Optical Engineering (OIOE 2025), edited by Yang Yue, Ming Jiang, and Qingyang Wei. SPIE, 2025. https://doi.org/10.1117/12.3068197.

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Zhou, Yanliang, Tianhe Liu, Jiawen Wang, and Jie Cheng. "Fast Classification Model Based on Genetic Algorithm and XGBoost-RandomForest Stacking Model." In 2024 14th International Conference on Information Science and Technology (ICIST). IEEE, 2024. https://doi.org/10.1109/icist63249.2024.10805304.

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Mukherjee, Dipendu, Shivam Chauhan, Sagarika Ghosh, and G. Uma Devi. "Enhancing Temporal Analysis of Jodhpur's Land Surface Temperature: A Comparative Study of XGBoost and Fine-Tuned XGBoost Model." In 2024 12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks (IEMECON). IEEE, 2024. https://doi.org/10.1109/iemecon62401.2024.10846243.

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Ma, Liang, Ruimin Niu, Wen Zhang, and Yan Mu. "Logistic Regression and XGBoost Model of Multiple Factors on Neijuan." In 2024 IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2024. http://dx.doi.org/10.1109/itnec60942.2024.10733149.

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Reports on the topic "XGBOOST MODEL"

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Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera, and Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2320.

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Nearly 5,000 people are killed and more than 418,000 are injured in weather-related traffic incidents each year. Assessments of the effectiveness of statistical models applied to crash severity prediction compared to machine learning (ML) and deep learning techniques (DL) help researchers and practitioners know what models are most effective under specific conditions. Given the class imbalance in crash data, the synthetic minority over-sampling technique for nominal (SMOTE-N) data was employed to generate synthetic samples for the minority class. The ordered logit model (OLM) and the ordered p
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