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Relevant bibliographies by topics / EXtreme gradient boosting (XGB) classifier

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Academic literature on the topic 'EXtreme gradient boosting (XGB) classifier'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "EXtreme gradient boosting (XGB) classifier"

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Mukhanova, Ayagoz, Madiyar Baitemirov, Azamat Amirov, et al. "Forecasting creditworthiness in credit scoring using machine learning methods." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 5 (2024): 5534. http://dx.doi.org/10.11591/ijece.v14i5.pp5534-5542.

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This article provides an overview of modern machine learning methods in the context of their active use in credit scoring, with particular attention to the following algorithms: light gradient boosting machine (LGBM) classifier, logistic regression (LR), linear discriminant analysis (LDA), decision tree (DT) classifier, gradient boosting classifier and extreme gradient boosting (XGB) classifier. Each of the methods mentioned is subject to careful analysis to evaluate their applicability and effectiveness in predicting credit risk. The article examines the advantages and limitations of each met

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Mousa, Saleh R., Peter R. Bakhit, and Sherif Ishak. "An extreme gradient boosting method for identifying the factors contributing to crash/near-crash events: a naturalistic driving study." Canadian Journal of Civil Engineering 46, no. 8 (2019): 712–21. http://dx.doi.org/10.1139/cjce-2018-0117.

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Despite the research efforts for reducing traffic accidents, the number of global annual vehicle accidents is still on the rise. This continues to motivate researchers to examine the factors contributing to crash and near-crash events (CNC). Recently, many studies attempted to identify the associated crash factors using naturalistic driving study (SHRP2-NDS) data. Despite the many classifiers developed in the literature, the high dimensionality and multicollinearity within the SHRP2-NDS data limit the accuracy and reliability of the developed models. This study develops an extreme gradient boo

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Chen, Pengzhen. "Research on Mushroom Classification Based on XGB Technology." Advances in Engineering Technology Research 13, no. 1 (2025): 1494. https://doi.org/10.56028/aetr.13.1.1494.2025.

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The mushroom classification problem, as a typical binary classification problem, has become a widely studied object in the field of machine learning. Traditional mushroom classification methods typically rely on manual feature extraction and rule-based criteria establishment, which are often susceptible to human factors, resulting in relatively low classification accuracy. With the development of machine learning technology, especially the emergence of ensemble learning methods, based on various machine learning models, particularly tree-based models, it is possible to efficiently distinguish

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Iqbal, Saqib, Azhar Imran, and Muhammad Adnan. "Breast Tumor Detection using Machine Learning Boosting Classifiers." Journal of Computing & Biomedical Informatics 4, no. 01 (2022): 118–31. http://dx.doi.org/10.56979/401/2022/64.

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Breast cancer is the frequently found in women and the second greatest reason of death worldwide. As breast cancer is detected early, the ratio of survival rate increases because better therapy may be provided. ML algorithms are very vital in the early diagnosis of breast cancer. In this study, we purposed a Novel method that increases the accuracy and performance using these three different classifiers: Gradient Boost (GB), Ada Boost (ABC), and Extreme Gradient Boost (XGB). On the Public dataset WBC, we evaluated and compared the classifiers’ performance and accuracy. Because the chance of ex

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Shao, Chen, and Yue zhong yi Sun. "Shilling attack detection for collaborative recommender systems: a gradient boosting method." Mathematical Biosciences and Engineering 19, no. 7 (2022): 7248–71. http://dx.doi.org/10.3934/mbe.2022342.

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<abstract> <p>Organized malicious shilling attackers influence the output of the collaborative filtering recommendation systems by inserting fake users into the rating matrix within the database. The existence of shilling attack poses a serious risk to the stability of the system. To counter this specific security threat, many attack detection methods are proposed. Some of the past methods suffer from two disadvantages, the first being that they only analyze the rating matrix from a single perspective of user rating values and ignore other perspectives. Another is that some methods

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Afolabi, Hassan A., and Abdurazzag A. Aburas. "Statistical performance assessment of supervised machine learning algorithms for intrusion detection system." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 266–77. https://doi.org/10.11591/ijai.v13.i1.pp266-277.

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Several studies have shown that an ensemble classifier's effectiveness is directly correlated with the diversity of its members. However, the algorithms used to build the base learners are one of the issues encountered when using a stacking ensemble. Given the number of options, choosing the best ones might be challenging. In this study, we selected some of the most extensively applied supervised machine learning algorithms and performed a performance evaluation in terms of well-known metrics and validation methods using two internet of things (IoT) intrusion detection datasets, namely network

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Kar, Subhajit, Rajorshi Bhattacharya, Ramkrishna Das, Ylva Pihlström, and Megan O. Lewis. "Classification of Wolf–Rayet Stars Using Ensemble-based Machine Learning Algorithms." Astrophysical Journal 977, no. 2 (2024): 170. https://doi.org/10.3847/1538-4357/ad8dda.

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Abstract We develop a robust machine learning classifier model utilizing the eXtreme-Gradient Boosting (XGB) algorithm for improved classification of Galactic Wolf–Rayet (WR) stars based on IR colors and positional attributes. For our study, we choose an extensive data set of 6555 stellar objects (from 2MASS and AllWISE data releases) lying in the Milky Way (MW) with available photometric magnitudes of different types, including WR stars. Our XGB classifier model can accurately (with an 86% detection rate) identify a sufficient number of WR stars against a large sample of non-WR sources. The X

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A. Alharbi, Lubna. "Heart Disease Prediction of Cleveland Clinic Patients using Advanced Machine Learning Algorithms." Journal of Advanced Research Design 126, no. 1 (2025): 1–14. https://doi.org/10.37934/ard.126.1.114.

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Globally, cardiovascular diseases (CVDs) constitute the primary cause of morbidity and mortality worldwide. Early diagnosis of those at risk of CVDs may lower the number of avoidable fatalities. It has been shown that machine learning (ML) is helpful in anticipating cardiac issues. Adoption of a prediction system that can detect cardiac diseases before they deteriorate would offer people worldwide enormous hope and help in decision-making. ML has become a popular technique for generating predictions from enormous real-world datasets. It has also been discovered that many ML classifiers contain

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Afolabi, Hassan A., and Aburas A. Abdurazzag. "Statistical performance assessment of supervised machine learning algorithms for intrusion detection system." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 266. http://dx.doi.org/10.11591/ijai.v13.i1.pp266-277.

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<span lang="EN-US">Several studies have shown that an ensemble classifier's effectiveness is directly correlated with the diversity of its members. However, the algorithms used to build the base learners are one of the issues encountered when using a stacking ensemble. Given the number of options, choosing the best ones might be challenging. In this study, we selected some of the most extensively applied supervised machine learning algorithms and performed a performance evaluation in terms of well-known metrics and validation methods using two internet of things (IoT) intrusion detection

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Saleem, Muniba, Waqar Aslam, Muhammad Ikram Ullah Lali, Hafiz Tayyab Rauf, and Emad Abouel Nasr. "Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis." Diagnostics 13, no. 22 (2023): 3441. http://dx.doi.org/10.3390/diagnostics13223441.

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Thalassemia represents one of the most common genetic disorders worldwide, characterized by defects in hemoglobin synthesis. The affected individuals suffer from malfunctioning of one or more of the four globin genes, leading to chronic hemolytic anemia, an imbalance in the hemoglobin chain ratio, iron overload, and ineffective erythropoiesis. Despite the challenges posed by this condition, recent years have witnessed significant advancements in diagnosis, therapy, and transfusion support, significantly improving the prognosis for thalassemia patients. This research empirically evaluates the e

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Book chapters on the topic "EXtreme gradient boosting (XGB) classifier"

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Das, Abhishek, Saumendra Kumar Mohapatra, and Mihir Narayan Mohanty. "Brain Image Classification Using Optimized Extreme Gradient Boosting Ensemble Classifier." In Biologically Inspired Techniques in Many Criteria Decision Making. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8739-6_20.

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Scoralick, João P., Gabriele C. Iwashima, Fernando A. B. Colugnati, Leonardo Goliatt, and Priscila V. S. Z. Capriles. "A Extreme Gradient Boosting Classifier for Predicting Chronic Kidney Disease Stages." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71187-0_83.

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Monika, Munish Kumar, and Manish Kumar. "XGBoost: 2D-Object Recognition Using Shape Descriptors and Extreme Gradient Boosting Classifier." In Computational Methods and Data Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6876-3_16.

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Datsi, Toufik, Khalid Aznag, and Ahmed El Oirrak. "Fashion Image Classification Using Convolutional Neural Network-VGG16 and eXtreme Gradient Boosting Classifier." In International Conference on Advanced Intelligent Systems for Sustainable Development. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26384-2_36.

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Mahanta, Soumya Ranjan, and Mrutyunjaya Panda. "Sports Prediction for Cricket Match Using Grid Search and Extreme Gradient Boosting Classifier." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-8160-7_13.

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Epalle, Thomas M., Yuqing Song, Hu Lu, and Zhe Liu. "Characterizing and Identifying Autism Disorder Using Regional Connectivity Patterns and Extreme Gradient Boosting Classifier." In Communications in Computer and Information Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36808-1_62.

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Liu, Guangyu, Xinying Qu, and Dongzhe Qu. "Application and Optimization of Artificial Intelligence in Distributed Energy Management Systems." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia241166.

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In order to solve the problem of inaccurate prediction performance of existing distributed energy management, the application and optimization of artificial intelligence in distributed energy management systems are proposed. This paper uses a lightweight gradient boosting machine model to conduct detailed analysis of energy data, and optimizes the LightGBM model through PSO to improve model performance. By predicting energy data, and conducting experimental comparisons with multiple linear regression (LR), random forest (RF), LightGBM and extreme gradient boosting (XGB). Experimental results s

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Hussain, Arif, and Gelli Ravikumar. "Machine Learning-Driven Stacked Ensemble Meta-Learning for Short-Term Solar Power Forecasting." In Smart Grids - Innovations for a Sustainable Future [Working Title]. IntechOpen, 2025. https://doi.org/10.5772/intechopen.1011406.

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This chapter presents an advanced short-term solar power forecasting method utilizing a machine learning-driven stacked ensemble meta-learning (ML-SEML) framework. The inherent intermittency of solar power poses a significant challenge to accurate forecasting, as traditional machine and deep learning models struggle to capture these fluctuations effectively. To address this issue, we propose a hybrid approach that integrates multiple canonical and ensemble models, including K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), Gradient Boosting Regressor (GB

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Abhinaya, Peryala, C. Kishor Kumar Reddy, Abhishek Ranjan, and Ozen Ozer. "Explicit Monitoring and Prediction of Hailstorms With XGBoost Classifier for Sustainability." In AI and IoT for Proactive Disaster Management. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-3896-4.ch006.

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Hailstorms are extremely dangerous for both people and property, hence precise forecasting techniques are required. To increase hailstorm forecast accuracy, this study suggests utilizing the XGBoost algorithm. The gradient boosting technique XGBoost is well-known for its effectiveness at managing intricate datasets and nonlinear relationships. The suggested approach improves prediction abilities by incorporating many meteorological factors and historical hailstorm data. The model outperforms conventional approaches through thorough evaluation utilizing cross-validation techniques. XGBoost, or

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Ahmed, Md Sakir, and Abhijit Bora. "A Comprehensive Approach for Using Hybrid Ensemble Methods for Diabetes Detection." In Critical Approaches to Data Engineering Systems and Analysis. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2260-4.ch001.

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This study is focused on the possible application of hybrid models as well as their usage in the detection of diabetes. This study focuses on various machine learning algorithms like Decision Trees, Random Forests, Logistic Regression, K-nearest neighbor, Support Vector Machines, Gaussian Naive Bayes, Adaptive Boosting Classifier, and Extreme Gradient Boosting as well as the usage of Stacking Classifier for the preparation of the hybrid model. An in-depth analysis was also made during this study to compare the traditional approach with the hybrid approach. Moreover, the usage of data augmentat

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Conference papers on the topic "EXtreme gradient boosting (XGB) classifier"

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Sai Varshith, Pabbisetty Venkata, and V. Parthipan. "An Effective Analysis of Predicting Clothes types in E-commerce reviews using CatBoost Algorithm with Extreme Gradient Boosting Classifier." In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES). IEEE, 2024. https://doi.org/10.1109/ic3tes62412.2024.10877465.

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Vieira, Ronald E., Farzin Darihaki, Jamie Li, and Siamack A. Shirazi. "Application of Machine Learning Techniques for Sand Erosion Prediction for Elbows in Multiphase Flow." In CONFERENCE 2023. AMPP, 2023. https://doi.org/10.5006/c2023-18995.

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Abstract The aim of this work is to define, implement, test, and validate an AI methodology using existing machine learning (ML) algorithms to predict sand erosion in 90° elbows for a broad range of multiphase operating conditions. Based on information obtained from the experimental UT wall thickness loss data collected for different flow regimes (gas-sand, liquid-sand, dispersed-bubble, churn, annular, and low liquid loading multiphase flows), the methodology has been developed to predict the maximum erosion magnitudes in standard metallic elbows. In order to expand the range of application o

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Zhang, Zhuoran, and Guanlan Liu. "A Prediction of Corrosion-related Leakage on Distribution Pipelines via Machine Learning Method." In CONFERENCE 2023. AMPP, 2023. https://doi.org/10.5006/c2023-18972.

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Abstract Distribution pipelines are system of main and service lines that transports the product to each individual home and business place. Typically, it operates at a lower pressure than transmission pipes, and it is not linear referenced in the database. In the meantime, distribution pipelines have more leak records available, which encourages the ability to do machine learning on them. This study applied machine learning methods, including the benchmark performance multiple linear regression (MLR) and decision tree-based extreme gradient boosting regression (XGB), to predict the corrosion-

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Pirić, David, and Romana Masnikosa. "PERFORMANCE OF RANDOM FORESTS, EXTREME GRADIENT BOOSTING AND SUPPORT VECTOR MACHINES EMPLOYED IN LIPIDOMICS." In 17th International Conference on Fundamental and Applied Aspects of Physical Chemistry. Society of Physical Chemists of Serbia, 2024. https://doi.org/10.46793/phys.chem24i.223p.

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Herein we present the performance of three supervised machine learning (ML) algorithms: random forests (RF), extreme gradient boosting (XGB) and support vector machines (SVM) in classification of human serum samples into pancreatic cancer or control group, using a lipidomic dataset retrieved from the research article „Lipidomic profiling of human serum enables detection of pancreatic cancer“ by Wolrab et al. [1]. Our main objective was to assess and compare, for the three ML techniques, the performance metrics, that is accuracy, precision, sensitivity, F1 score and ROC- AUC, with those compute

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Osa, Edosa, Enosa Iyekekpolo, Augustus Ibhaze, Wilson Sadoh, and Patience Orukpe. "Implementing Stratified K-Fold Technique for Data Intrusion Detection Models in Internet of Health Things Systems." In International Conference on Artificial Intelligence and Robotics. Machine Intelligence Research Group (MIRG), 2024. https://doi.org/10.52968/15061492.

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Due to the contemporary prevalence of evolving cutting-edge computerized technologies in virtually every sphere of modern day human activities, many of such interventions find their use in the medical domain. One such technology that is widely adopted is the Internet of Things, which when implemented in the medical industry is dubbed Internet of Health Things (IoHT) or Internet of Medical Things (IoMT). Such systems are widely adopted due to the increased mobility and dynamism they introduce to the dispensing of health services. They therefore are repositories of a lot of medical data, be it p

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Azevedo, Karolayne, Luísa Souza, Matheus Dalmolin, and Marcelo Fernandes. "IA explicável aplicada para identificar genes influentes na classificação do câncer por meio de dados de expressão gênica de RNA-Seq." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2023. http://dx.doi.org/10.21528/cbic2023-096.

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Este artigo faz uso de três técnicas de aprendizagem de máquina (Machine Learnig – ML) para classificar os cinco tipos de câncer mais recorrentes em mulheres, a partir de dados de expressão gênica de RNA-Seq. Os desafios incluem: alta dimensionalidade do conjunto de dados e a falta de transparência dos modelos de ML. Para mitigar esses problemas, foi utilizado a técnica SHAP (SHapley Additive exPlanations) que e uma técnica de inteligência artificial explicável (Explainable artificial intelligence – XAI) utilizada para compreender como esses modelos tomam decisões podendo ser usada como uma es

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Tan, Jie Ying, and Andy Sai Kit Chow. "Sentiment Analysis on Game Reviews: A Comparative Study of Machine Learning Approaches." In International Conference on Digital Transformation and Applications (ICDXA 2021). Tunku Abdul Rahman University College, 2021. http://dx.doi.org/10.56453/icdxa.2021.1023.

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Sentiment analysis is one of the major topics of natural language processing which is used to determine whether data is positive, negative or neutral. It is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback to understand their customers’ needs. This paper explores various machine learning algorithms including Logistic Regression (LR), Multinomial Naïve Bayes (MNB), Support Vector Classifier (SVC), Multi-layer Perceptron Classifier (MLP) and Extreme Gradient Boosting Classifier (XGB) to build sentiment analysis models tailored for the ga

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Latrache, Houda, Mounira Ouarzeddine, Boularbah Souissi, and Ikram Hammouda. "PolSAR Data Classification Using Extreme Gradient Boosting Classifier." In 2023 International Conference on Earth Observation and Geo-Spatial Information (ICEOGI). IEEE, 2023. http://dx.doi.org/10.1109/iceogi57454.2023.10292971.

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Tahsin, Tasfia, Khondoker Mirazul Mumenin, Farhana Tazmim Pinki, et al. "GWO-XGB: Grey Wolf Optimization-based eXtreme Gradient Boosting for Hypertension Prediction in Bangladesh." In 2021 International Conference on Electronics, Communications and Information Technology (ICECIT). IEEE, 2021. http://dx.doi.org/10.1109/icecit54077.2021.9641256.

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PERIN, M. "Extreme gradient boosting (XGB)-driven simulator for radial-axial force estimation in ring rolling." In Material Forming. Materials Research Forum LLC, 2025. https://doi.org/10.21741/9781644903599-90.

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Abstract. Force estimation in metal forming processes is crucial for identifying the correct machine during production and determining the best process parameters to achieve good quality, reduce scrap and minimize energy consumption. In the hot Radial-Axial Ring Rolling (RARR) process force estimation is normally carried out by highly time-consuming finite element analysis (FEA) or empiric-analytical models. To achieve a reliable and yet real-time estimation of the process loads, this research presents a freeware software based on a Python script, to estimate mandrel and axial roll forces base

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Reports on the topic "EXtreme gradient boosting (XGB) classifier"

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Liu, Hongrui, and Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2102.

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In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accident

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