Relevant bibliographies by topics / XGBooster

Contents

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

Academic literature on the topic 'XGBooster'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "XGBooster"

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YILDIRIM, Ahmet, and Ali GÜNEŞ. "Predicting Stock Prices Using Machine Learning." International Journal of Engineering Research and Applications 14, no. 7 (2024): 81–88. http://dx.doi.org/10.9790/9622-14078188.

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Predicting the closing prices of stock instruments, which have become one of the prominent investment tools in our era, has become an important topic. Investors tend to use their investments to gain profit with minimal risk. Accordingly, predicting the closing prices of stocks based on future closing values is crucial. Two different methods are used for these decisions: fundamental analysis and technical analysis. With the rapidly developing software and hardware technology, the use of statistical methods in technical analysis is rapidly increasing. In this study, the closing values of APPLE,
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Bechoo Lal, Prof. Th Basanta, and Dr. Mutum Vidyarani Devi. "Exploring the NSL-KDD Dataset: A Comprehensive Analysis about Intrusion Detection System (IDS)." International Journal of Scientific Research in Science and Technology 12, no. 2 (2025): 715–22. https://doi.org/10.32628/ijsrst251222614.

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In this research article the researcher emphasized the Network threats and hazards are evolving at a high-speed rate in recent years. Many mechanisms (such as firewalls, anti-virus, anti-malware, and spam filters) are being used as security tools to protect networks. An intrusion detection system (IDS) is also an effective and powerful network security system to detect unauthorized and abnormal network traffic flow. This article presents a review of the research trends in network-based intrusion detection systems (NIDS), their approaches, and the most common datasets used to evaluate IDS 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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Riando, Dhafin, and Afiyati Afiyati. "Implementasi Algoritma XGBoost untuk Memprediksi Harga Jual Cabai Rawit di DKI Jakarta." Eduvest - Journal of Universal Studies 4, no. 9 (2024): 7877–89. http://dx.doi.org/10.59188/eduvest.v4i9.3784.

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This research focuses on applying the XGBoost algorithm to analyze and predict cayenne pepper prices. The main aim is to exploit XGBoost's exceptional capability to manage large datasets and discern intricate patterns for precise price forecasting. The dataset comprises historical cayenne pepper price data, along with pertinent economic and climatic factors. The XGBoost model was developed and validated on this dataset, with its performance assessed using metrics. The results indicated a high level of accuracy, achieving an R² score of 99% on the training set and 92% on the test set, reflectin
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Srinivasan, Bhavana. "Comparative Analysis of LightGBM and XGBoost for Predictive Risk Assessment in Blockchain Transactions within the Metaverse." Journal of Current Research in Blockchain 2, no. 1 (2025): 1–12. https://doi.org/10.47738/jcrb.v2i1.23.

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The growing integration of blockchain technology within the metaverse has created an urgent need for effective systems to assess and mitigate transaction risks. This study investigates the use of machine learning models, specifically LightGBM and XGBoost, for predictive risk analysis in blockchain transactions. A dataset comprising 50,000 blockchain transactions, with 75% categorized as low-risk and 25% as high-risk, was used to evaluate the performance of these models across key metrics. LightGBM emerged as the superior model, achieving an accuracy of 91.2%, surpassing XGBoost's 89.5%. Additi
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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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Makarand, Bhosale Sakshi. "Machine Learning Enabled Inventory Prediction." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35109.

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Effective inventory management is a critical aspect of business operations, ensuring optimal stock levels, minimizing costs, and meeting customer demand. Accurate inventory prediction plays a pivotal role in achieving these objectives. This project explores the application of the XGBoost algorithm, a powerful machine learning technique, for inventory prediction. XGBoost's ability to handle complex nonlinear relationships and its robust performance make it a promising approach for this task. The project aims to develop an inventory prediction system using the XGBoost algorithm, leveraging histo
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Makarand, Bhosale Sakshi. "Machine Learning Enabled Inventory Prediction." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30009.

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Effective inventory management is a critical aspect of business operations, ensuring optimal stock levels, minimizing costs, and meeting customer demand. Accurate inventory prediction plays a pivotal role in achieving these objectives. This project explores the application of the XGBoost algorithm, a powerful machine learning technique, for inventory prediction. XGBoost's ability to handle complex nonlinear relationships and its robust performance make it a promising approach for this task. The project aims to develop an inventory prediction system using the XGBoost algorithm, leveraging histo
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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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Dissertations / Theses on the topic "XGBooster"

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Alhomsi, Moaz, and Hinda Ahmed. "Forecasting of ExchangeRate: Autoregressive modelsvs. XGBoost." Thesis, Jönköping University, IHH, Nationalekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-51370.

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In international economics and trading, the exchange rate is important. Forecasting theexchange rate helps in minimizing risks and maximizing profits. The study attempts to test threemodels to forecast EUR/USD exchange rate. Based on previous work by Meese & Rogoff(1983), we replicated the authors work of the Random Walk model of AR(1) on different periodand currency to check if the model was able to forecast the exchange rate. Then we ran ARDLand XGBoost models to find which of the two models performed better than the Random Walkmodel based on different measures. The measures are Root Mea
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Blomqvist, Johanna. "Using XGBoost to classify theBeihang Keystroke Dynamics Database." Thesis, Uppsala universitet, Datalogi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-357420.

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Keystroke Dynamics enable biometric security systems by collecting and analyzing computer keyboard usage data. There are different approaches to classifying keystroke data and a method that has been gaining a lot of attention in the machine learning industry lately is the decision tree framework of XGBoost. XGBoost has won several Kaggle competitions in the last couple of years, but its capacity in the keystroke dynamics field has not yet been widely explored. Therefore, this thesis has attempted to classify the existing Beihang Keystroke Dynamics Database using XGBoost. To do this, keystroke
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Salam, Patrous Ziad. "Evaluating XGBoost for User Classification by using Behavioral Features Extracted from Smartphone Sensors." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233522.

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Smartphones have opened the possibility to interact with people anytime and anywhere. A significant amount of individuals rely on their smartphone for work-related and everyday tasks. As a consequence modern smartphones include sensitive, valuable and confidential information, such as e-mails, photos, notes, and messages. The primary concern is to prevent unauthorized access to data stored on the smartphone and applications. Traditional authentication methods are entry-point based and do not support continuous authorization. Therefore, as long as the session is active, there are no mechanisms
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Elena, Podasca. "Predicting the Movement Direction of OMXS30 Stock Index Using XGBoost and Sentiment Analysis." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21119.

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Background. Stock market prediction is an active yet challenging research area. A lot of effort has been put in by both academia and practitioners to produce accurate stock market predictions models, in the attempt to maximize investment objectives. Tree-based ensemble machine learning methods such as XGBoost have proven successful in practice. At the same time, there is a growing trend to incorporate multiple data sources in prediction models, such as historical prices and text, in order to achieve superior forecasting performance. However, most applications and research have so far focused o
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Hesselroth, Johannes. "Väderdata, Inverse Distance Weighting och XGBoost för prediktering av väglagsfriktion : En utforskande studie." Thesis, Karlstads universitet, Handelshögskolan (from 2013), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-74530.

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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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Cerna, Ñahuis Selene Leya. "Comparative analysis of XGBoost, MLP and LSTM techniques for the problem of predicting fire brigade Iiterventions /." Ilha Solteira, 2019. http://hdl.handle.net/11449/190740.

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Orientador: Anna Diva Plasencia Lotufo<br>Abstract: Many environmental, economic and societal factors are leading fire brigades to be increasingly solicited, and, as a result, they face an ever-increasing number of interventions, most of the time on constant resource. On the other hand, these interventions are directly related to human activity, which itself is predictable: swimming pool drownings occur in summer while road accidents due to ice storms occur in winter. One solution to improve the response of firefighters on constant resource is therefore to predict their workload, i.e., their n
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Hardin, Maria, and Joakim Wellenstam. "Utveckling av komplexitetsbedömningsmodell med XGBoost för att anpassa text-till-talsyntes inom Robot-Assisted Language Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-263693.

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Den sociala roboten Furhat har visat sig vara relevant för Robot-Assisted Language Learning. I dagsläget har Furhat en konstant talhastighet, men i tidigare studier har det framkommit att den skulle gynnas av att ha en mer dynamisk text-till-talsyntes. I denna kandidatexamensuppsats har en modell skapats, i syfte att klassificera meningar på svenska efter komplexitet. Modellen användes för att avgöra vilka meningar som bör ha en lägre talhastighet, i syfte att öka deras begripligheten för elever som lär sig svenska som andraspråk. För att utvärdera modellen jämfördes komplexitetsbedömningen me
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Федоров, Д. П. "Comparison of classifiers based on the decision tree." Thesis, ХНУРЕ, 2021. https://openarchive.nure.ua/handle/document/16430.

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The main purpose of this work is to compare classifiers. Random Forest and XGBoost are two popular machine learning algorithms. In this paper, we looked at how they work, compared their features, and obtained accurate results from their robots.
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Калайчев, Г. В. "Machine learning in classification tasks." Thesis, ХНУРЕ, 2021. https://openarchive.nure.ua/handle/document/16433.

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The main goal of this work is to show the ways to use machine learning algorithms to solve classification tasks. One of the most efficient algorithms is Gradient Boosting (XGB Classier). This is a method which is usually used in competitions because of his speed and opportunity to work with big amount of data.
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Books on the topic "XGBooster"

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Xgboost. the Extreme Gradient Boosting for Mining Applications. GRIN Verlag GmbH, 2018.

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Quinto, Butch. Next-Generation Machine Learning with Spark: Covers XGBoost, LightGBM, Spark NLP, Distributed Deep Learning with Keras, and More. Apress, 2020.

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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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Hands-On Gradient Boosting with XGBoost and Scikit-learn: Perform Accessible Machine Learning and Extreme Gradient Boosting with Python. Packt Publishing, Limited, 2020.

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Book chapters on the topic "XGBooster"

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de Souza, Fernanda Maria, Julia Grando, and Fabiano Baldo. "Adaptive Fast XGBoost for Regression." In Intelligent Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21686-2_7.

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Gouveia, Arnaldo, and Miguel Correia. "Network Intrusion Detection with XGBoost." In Recent Advances in Security, Privacy, and Trust for Internet of Things (IoT) and Cyber-Physical Systems (CPS). Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9780429270567-6.

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Montassar, Imen, Belkacem Chikhaoui, and Shengrui Wang. "Agitated Behaviors Detection in Children with ASD Using Wearable Data." In Digital Health Transformation, Smart Ageing, and Managing Disability. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43950-6_8.

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AbstractChildren diagnosed with Autism Spectrum Disorder (ASD) often exhibit agitated behaviors that can isolate them from their peers. This study aims to examine if wearable data, collected during everyday activities, could effectively detect such behaviors. First, we used the Empatica E4 device to collect real data including Blood Volume Pulse (BVP), Electrodermal Activity (EDA), and Acceleration (ACC), from a 9-years-old male child with autism over 6 months. Second, we analyzed and extracted numerous features from each signal, and employed different classifiers including Support Vector Mach
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Abbasi, Raza Abid, Nadeem Javaid, Muhammad Nauman Javid Ghuman, Zahoor Ali Khan, Shujat Ur Rehman, and Amanullah. "Short Term Load Forecasting Using XGBoost." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15035-8_108.

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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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Arora, Renuka, and Sunny Arora. "Speech impairment recognition using XGBoost classifier." In Artificial Intelligence and Speech Technology. CRC Press, 2021. http://dx.doi.org/10.1201/9781003150664-21.

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Korstanje, Joos. "Gradient Boosting with XGBoost and LightGBM." In Advanced Forecasting with Python. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7150-6_15.

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Yang, Wenhan, Boyu Sun, and Banruo Liu. "Basic Profiling Extraction Based on XGBoost." In Communications in Computer and Information Science. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0713-5_7.

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Hussain, Nisar, Amna Qasim, Zia-ud-din Akhtar, et al. "Stock Market Performance Analytics Using XGBoost." In Advances in Computational Intelligence. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47765-2_1.

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Ghosh, Samyadeep, Dinesh Borse, Lakshmi Ram Kiran Padilam, et al. "Application of XGBoost in Flood Modeling." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-9168-2_16.

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Conference papers on the topic "XGBooster"

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Quyet, Nguyen Huu, Tran Ngoc Hoa, Nguyen Ngoc Lan, and Bui Tien Thanh. "Damage Detection for Truss Bridge Structure Using XGBoost." In The 12th International Conference on Fracture Fatigue and Wear. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-jdc4vd.

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Structural health monitoring (SHM) is a burgeoning area of interest among modern research endeavors, motivated by the application of state-of-the-art machine learning models. During the last few years, many researchers have proposed techniques for the analysis of SHM datasets, particularly those corresponding to sequence data collected from sensors. Following the flow of this research, in this work, we introduce an effective approach utilizing eXtreme Gradient Boosting (XGBoost), a potent ensemble learning framework rooted in gradient boosting for damage detection. A dataset of damage cases fr
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Nageswari, Akula, U. Jyothi, G. Divya, T. Ammannamma, and V. Usha. "Water Quality Classification using XGBoost method." In 2024 IEEE 6th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA). IEEE, 2024. https://doi.org/10.1109/icccmla63077.2024.10871422.

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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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Husin, Husna Sarirah, Afizan Azman, Norhidayah Hamzah, and Pravin A.-L. Anand Kumar. "Early Prediction of Stroke using XGBoost classification." In 2024 International Visualization, Informatics and Technology Conference (IVIT). IEEE, 2024. http://dx.doi.org/10.1109/ivit62102.2024.10692877.

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Kumar, Suresh, Jyoti Prakash Singh, and Shantanu. "Genre-Based Movie Recommender System with XGBoost." In 2024 4th International Conference on Computer, Communication, Control & Information Technology (C3IT). IEEE, 2024. https://doi.org/10.1109/c3it60531.2024.10829451.

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Zhao, Wenchuan, Qi Zhang, and Ting Shu. "Air Pollution Source Tracing by VIME-XGBoost." In 2025 7th International Symposium on Computational and Business Intelligence (ISCBI). IEEE, 2025. https://doi.org/10.1109/iscbi64586.2025.11015369.

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Lam, Ka Nam. "Traffic Prediction Using LSTM, RF and XGBoost." In International Conference on Data Analysis and Machine Learning. SCITEPRESS - Science and Technology Publications, 2024. https://doi.org/10.5220/0013515600004619.

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Zhao, Hui, and Ling Zhou. "Emotion Recognition Based on XGBoost and HRV." In 2024 3rd International Conference on Health Big Data and Intelligent Healthcare (ICHIH). IEEE, 2024. https://doi.org/10.1109/ichih63459.2024.11064780.

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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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Luqman, Muhammad, and Muhammad Arif. "Interpretable Machine Learning Modeling for Surface Chloride Concentration Prediction in Marine Concrete." In 14th International Civil Engineering Conference. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-r9up7x.

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The chloride concentration (Cs) on the concrete surface is an indispensable metric for designing resilience and estimating the lifespan of concrete structures in aquatic settings. Consequently, due to Chloride action, many reinforced concrete constructions cannot reach their intended or planned life span and experience early degradation. This study utilizes three independent machine learning techniques, Extreme Gradient Boosting (XGBoost), Multi Expression Programming (MEP), and Decision Tree (DT), to forecast the concrete’s surface chloride concentration (Cs). To achieve this objective, a com
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Reports on the topic "XGBooster"

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Gu, Xiaofeng, A. Fedotov, and D. Kayran. Application of a machine learning algorithm (XGBoost) to offline RHIC luminosity optimization. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1777441.

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Gu, Xiaofeng, T. Kanesue, M. Okamura, et al. EBIS BEAM INTENSITY ONLINE OPTIMIZATION WITH GPTUNE AND OFFLINE ANALYSIS WITH XGBOOST. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2331235.

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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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