Relevant bibliographies by topics / Extreme Gradient Boosting Classifier

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

Academic literature on the topic 'Extreme Gradient Boosting Classifier'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Extreme Gradient Boosting Classifier"

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Abdualjabar, Rana Dhia’a, and Osama A. Awad. "Parallel extreme gradient boosting classifier for lung cancer detection." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1610. http://dx.doi.org/10.11591/ijeecs.v24.i3.pp1610-1617.

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Most lung cancers do not cause symptoms until the disease is in its later stage. That led the lung cancer having a high fatality rate compared to other cancer types. Many scientists try to use artificial intelligence algorithms to produce accurate lung cancer detection. This paper used extreme gradient boosting (XGBoost) models as a base model for its effectiveness. It enhanced lung cancer detection performance by suggesting three stages model; feature stage, XGBooste parallel stage and selection stage. This study used two types of gene expression datasets; RNA-sequence and microarray profiles
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Abdu-Aljabar, Rana Dhiaa, and Osama A. Awad. "Parallel extreme gradient boosting classifier for lung cancer detection." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1610–17. https://doi.org/10.11591/ijeecs.v24.i3.pp1610-1617.

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Most lung cancers do not cause symptoms until the disease is in its later stage. That led the lung cancer having a high fatality rate compared to other cancer types. Many scientists try to use artificial intelligence algorithms to produce accurate lung cancer detection. This paper used extreme gradient boosting (XGBoost) models as a base model for its effectiveness. It enhanced lung cancer detection performance by suggesting three stages model; feature stage, XGBooste parallel stage and selection stage. This study used two types of gene expression datasets; RNA-sequence and microarray profiles
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Shakya, Achala, Mantosh Biswas, and Mahesh Pal. "Fusion and Classification of SAR and Optical Data Using Multi-Image Color Components with Differential Gradients." Remote Sensing 15, no. 1 (2023): 274. http://dx.doi.org/10.3390/rs15010274.

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This paper proposes a gradient-based data fusion and classification approach for Synthetic Aperture Radar (SAR) and optical image. This method is used to intuitively reflect the boundaries and edges of land cover classes present in the dataset. For the fusion of SAR and optical images, Sentinel 1A and Sentinel 2B data covering Central State Farm in Hissar (India) was used. The major agricultural crops grown in this area include paddy, maize, cotton, and pulses during kharif (summer) and wheat, sugarcane, mustard, gram, and peas during rabi (winter) seasons. The gradient method using a Sobel op
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Parente, Daniel J. "PolyBoost: An enhanced genomic variant classifier using extreme gradient boosting." PROTEOMICS – Clinical Applications 15, no. 2-3 (2021): 1900124. http://dx.doi.org/10.1002/prca.201900124.

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Setyarini, Dela Ananda, Agnes Ayu Maharani Dyah Gayatri, Christian Sri Kusuma Aditya, and Didih Rizki Chandranegara. "Stroke Prediction with Enhanced Gradient Boosting Classifier and Strategic Hyperparameter." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 23, no. 2 (2024): 477–90. http://dx.doi.org/10.30812/matrik.v23i2.3555.

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A stroke is a medical condition that occurs when the blood supply to the brain is interrupted. Stroke can cause damage to the brain that can potentially affect a person's function and ability to move, speak, think, and feel normally. The effect of stroke on health emphasizes the importance of stroke detection, so an effective model is needed in predicting stroke. This research aimed to find a new approach that can improve the performance of stroke prediction by comparing four derivative algorithms from Gradient Boosting by adding hyperparameters tuning. The addition of hyperparameters was used
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Pratiwi, Nor Kumalasari Caecar, Hilal Tayara, and Kil To Chong. "An Ensemble Classifiers for Improved Prediction of Native–Non-Native Protein–Protein Interaction." International Journal of Molecular Sciences 25, no. 11 (2024): 5957. http://dx.doi.org/10.3390/ijms25115957.

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In this study, we present an innovative approach to improve the prediction of protein–protein interactions (PPIs) through the utilization of an ensemble classifier, specifically focusing on distinguishing between native and non-native interactions. Leveraging the strengths of various base models, including random forest, gradient boosting, extreme gradient boosting, and light gradient boosting, our ensemble classifier integrates these diverse predictions using a logistic regression meta-classifier. Our model was evaluated using a comprehensive dataset generated from molecular dynamics simulati
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Panigrahi, Millee, Dayal Kumar Behera, and Krishna Chandra Patra. "Epileptic seizure classification of electroencephalogram signals using extreme gradient boosting classifier." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 884–91. https://doi.org/10.11591/ijeecs.v25.i2.pp884-891.

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Epilepsy causes repeated seizures in an individual's life, which causes transient irregularities in the brain's electrical activity. It results in different physical symptoms that are abnormal. Various antiepileptic drugs fail to minimize repeated patient seizures. The electroencephalogram (EEG) signal recordings provide us with time-series data set for epileptic seizure detection and analysis. These signals are highly nonlinear and inconsistent, and they are recorded over time. Predicting the ictal period (seizure period at the time of epilepsy) is thus a challenging task in the naked
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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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Panigrahi, Millee, Dayal Kumar Behera, and Krishna Chandra Patra. "Epileptic seizure classification of electroencephalogram signals using extreme gradient boosting classifier." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 884. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp884-891.

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Epilepsy causes repeated seizures in an individual's life, which causes transient irregularities in the brain's electrical activity. It results in different physical symptoms that are abnormal. Various antiepileptic drugs fail to minimize repeated patient seizures. The electroencephalogram (EEG) signal recordings provide us with time-series data set for epileptic seizure detection and analysis. These signals are highly nonlinear and inconsistent, and they are recorded over time. Predicting the ictal period (seizure period at the time of epilepsy) is thus a challenging task in the naked eye for
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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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Dissertations / Theses on the topic "Extreme Gradient Boosting Classifier"

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Nikolaou, Nikolaos. "Cost-sensitive boosting : a unified approach." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/costsensitive-boosting-a-unified-approach(ae9bb7bd-743e-40b8-b50f-eb59461d9d36).html.

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In this thesis we provide a unifying framework for two decades of work in an area of Machine Learning known as cost-sensitive Boosting algorithms. This area is concerned with the fact that most real-world prediction problems are asymmetric, in the sense that different types of errors incur different costs. Adaptive Boosting (AdaBoost) is one of the most well-studied and utilised algorithms in the field of Machine Learning, with a rich theoretical depth as well as practical uptake across numerous industries. However, its inability to handle asymmetric tasks has been the subject of much criticis
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Al-Mter, Yusur. "Automatic Prediction of Human Age based on Heart Rate Variability Analysis using Feature-Based Methods." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166139.

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Heart rate variability (HRV) is the time variation between adjacent heartbeats. This variation is regulated by the autonomic nervous system (ANS) and its two branches, the sympathetic and parasympathetic nervous system. HRV is considered as an essential clinical tool to estimate the imbalance between the two branches, hence as an indicator of age and cardiac-related events.This thesis focuses on the ECG recordings during nocturnal rest to estimate the influence of HRV in predicting the age decade of healthy individuals. Time and frequency domains, as well as non-linear methods, are explored to
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Zhang, Yi. "Strategies for Combining Tree-Based Ensemble Models." NSUWorks, 2017. http://nsuworks.nova.edu/gscis_etd/1021.

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Ensemble models have proved effective in a variety of classification tasks. These models combine the predictions of several base models to achieve higher out-of-sample classification accuracy than the base models. Base models are typically trained using different subsets of training examples and input features. Ensemble classifiers are particularly effective when their constituent base models are diverse in terms of their prediction accuracy in different regions of the feature space. This dissertation investigated methods for combining ensemble models, treating them as base models. The goal is
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Andeta, Jemal Ahmed. "Road-traffic accident prediction model : Predicting the Number of Casualties." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20146.

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Efficient and effective road traffic prediction and management techniques are crucial in intelligent transportation systems. It can positively influence road advancement, safety enhancement, regulation formulation, and route planning to save living things in advance from road traffic accidents. This thesis considers road safety by predicting the number of casualties if an accident occurs using multiple traffic accident attributes. It helps individuals (drivers) or traffic offices to adjust and control their contributions for the occurrence of an accident before emerging it. Three candidate alg
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Sseguya, Raymond. "Forecasting anomalies in time series data from online production environments." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166044.

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Anomaly detection on time series forecasts can be used by many industries in especially forewarning systems that can predict anomalies before they happen. Infor (Sweden) AB is software company that provides Enterprise Resource Planning cloud solutions. Infor is interested in predicting anomalies in their data and that is the motivation for this thesis work. The general idea is firstly to forecast the time series and then secondly detect and classify anomalies on the forecast. The first part is time series forecasting and the second part is anomaly detection and classification done on the forec
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Oldenkamp, Emiel. "Using supervised learning methods to predict the stop duration of heavy vehicles." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-50977.

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In this thesis project, we attempt to predict the stop duration of heavy vehicles using data based on GPS positions collected in a previous project. All of the training and prediction is done in AWS SageMaker, and we explore possibilities with Linear Learner, K-Nearest Neighbors and XGBoost, all of which are explained in this paper. Although we were not able to construct a production-grade model within the time frame of the thesis, we were able to show that the potential for such a model does exist given more time, and propose some suggestions for the paths one can take to improve on the endpo
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Peng, I.-Hsuan, and 彭毅軒. "Gradient Boosting Classifier based on Gaussian Process." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/uyjc7c.

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碩士<br>國立臺灣大學<br>資訊管理學研究所<br>107<br>Gaussian process (GP) is mainly used to solve regression and classification problems in machine learning. GP is a nonparametric model with good prediction performance and wide applications. However, GP has an obvious drawback: high time complexity. This drawback makes it inappropriate for large data. In this work, we combine Gaussian process and gradient boosting to form Gradient Boosting Gaussian Process Classifier (GBGPC), then apply it to classification problems. The experiment results show that the proposed algorithm can largely improve training efficienc
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ZHUANG, BO-SHENG, and 莊博勝. "Demand Forecasting of Notebook Component Spare parts by Using Extreme Gradient Boosting." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/kd26za.

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碩士<br>國立臺北科技大學<br>工業工程與管理系<br>107<br>In recent years, due to the slowdown in the growth of notebook computers and tablet PCs, the performance of major brands has fallen into a bottleneck in research and developments. Since notebook computers are still high-priced products, and products become more sophisticated than desktop computers. In addition, the differences in products make assembly and maintenance to different degrees of difficulty, leading to an extremely high competition in the notebook market. In the past decade, notebook computer repair components often suffered from out of stock or
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Wu, Guan-Jhih, and 吳冠鋕. "Constructing a Credit Risk Assessment Model for Financial Institution by eXtreme Gradient Boosting Decision Tree." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/4sgzcr.

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Ou, Ming-Hong, and 歐明鴻. "Constructing a TFT-LCD Panel Classification Model for Automatic Optical Inspection using eXtreme Gradient Boosting Decision Tree." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/52npc3.

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碩士<br>國立交通大學<br>工業工程與管理系所<br>105<br>Thin film transistor liquid crystal display (TFT-LCD) panel is a key component in many electronic products. Its quality determines the value of follow-up products. Automatic optical inspection (AOI) plays an important role in Industry 4.0 and it has been introduced to screen out bad TFT-LCD panels during quality inspection. However most of the exist literature related to applying AOI in TFT-LCD panel inspection focus on defects like scratches, Mura, particles, etc. Few of them studied defective pixels on TFT-LCD panel. Therefore, this study aims to apply
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Books on the topic "Extreme Gradient Boosting Classifier"

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

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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 "Extreme Gradient Boosting 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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Shi, Heng, Belkacem Chikhaoui, and Shengrui Wang. "Tree-Based Models for Pain Detection from Biomedical Signals." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09593-1_14.

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AbstractFor medical treatments, pain is often measured by self-report. However, the current subjective pain assessment highly depends on the patient’s response and is therefore unreliable. In this paper, we propose a physiological-signals-based objective pain recognition method that can extract new features, which have never been discovered in pain detection, from electrodermal activity (EDA) and electrocardiogram (ECG) signals. To discriminate the absence and presence of pain, we establish four classification tasks and build four tree-based classifiers, including Random Forest, Adaptive Boost
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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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Jain, Mehul, Manas Suryabhan Patil, and Chandra Mohan. "Extreme Gradient Boosting for Toxic Comment Classification." In Computational Methods and Data Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3015-7_29.

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Chakrabarty, Navoneel, Tuhin Kundu, Sudipta Dandapat, Apurba Sarkar, and Dipak Kumar Kole. "Flight Arrival Delay Prediction Using Gradient Boosting Classifier." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1498-8_57.

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Conference papers on the topic "Extreme Gradient Boosting 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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Sakanti, Maria Mahardini, Viacheslav Siniaev, Aurelia Amaris, Win-Jet Luo, Suhartono, and C. Bambang Dwi Kuncoro. "Psychological Stress Classification Using Extreme Gradient Boosting Algorithm." In 2024 15th International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2024. https://doi.org/10.1109/ictc62082.2024.10827020.

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Likhitha, Gangavarapu, Bommu Ramya Sree, Chiluvuru Ratan, Karthikeyan C, and G. V. Samkumar. "Advancing Brain Tumor Classification Using CNN and eXtreme Gradient Boosting." In 2024 International Conference on Expert Clouds and Applications (ICOECA). IEEE, 2024. http://dx.doi.org/10.1109/icoeca62351.2024.00172.

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Yin, Zhe, Zhenfei Yan, Teng Liu, and Daying Lu. "Predicting microRNA-disease association by autoencoder and extreme gradient boosting." In 2024 Fourth International Conference on Biomedicine and Bioinformatics Engineering (ICBBE 2024), edited by Pier Paolo Piccaluga, Ahmed El-Hashash, and Xiangqian Guo. SPIE, 2024. http://dx.doi.org/10.1117/12.3044089.

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Deng, Xiaoyan, and Xiaobin Zhang. "Extreme gradient boosting algorithm in employment prediction of college graduates." In Second International Conference on Big Data, Computational Intelligence and Applications (BDCIA 2024), edited by Sos S. Agaian. SPIE, 2025. https://doi.org/10.1117/12.3059120.

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Nancy, P., Prachi Pandi, and Amrita Kumari. "Sleeping Pattern Analysis Using Extreme Gradient Boosting and Logistic Regression." In 2024 International Conference on IoT, Communication and Automation Technology (ICICAT). IEEE, 2024. https://doi.org/10.1109/icicat62666.2024.10923026.

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Y, Sowjanya Kumari, Jayakrishna K, Dhanasekar J, Miniappan PK, Balasundaram N, and Vijayakumari G. "Utilizing Extreme Gradient Boosting (XGBoost) for Apple Scab Disease Detection." In 2025 3rd International Conference on Advancement in Computation & Computer Technologies (InCACCT). IEEE, 2025. https://doi.org/10.1109/incacct65424.2025.11011325.

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Soni, Tanishq, Deepali Gupta, and Mudita Uppal. "Enhancing Cardiovascular Health through Gradient Boosting Classifier-Based Risk Prediction." In 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS). IEEE, 2024. https://doi.org/10.1109/icuis64676.2024.10866152.

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Rizqie, Muhammad Qurhanul, Iche A. Liberty, Indri S. Septadina, Pacu Putra, and Dian Palupi Rini. "Prediabetes Detection Using Non-Laboratory Data with Extreme Gradient Boosting Algorithm." In 2024 11th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI). IEEE, 2024. https://doi.org/10.1109/eecsi63442.2024.10776354.

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Oumiguil, Lahoucine, and Ali Nejmi. "A daily PV Plant Power Forecasting Using eXtreme Gradient Boosting Algorithm." In 2025 5th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET). IEEE, 2025. https://doi.org/10.1109/iraset64571.2025.11008237.

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Reports on the topic "Extreme Gradient Boosting 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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Forteza, Nicolás, and Sandra García-Uribe. A Score Function to Prioritize Editing in Household Survey Data: A Machine Learning Approach. Banco de España, 2023. http://dx.doi.org/10.53479/34613.

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Errors in the collection of household finance survey data may proliferate in population estimates, especially when there is oversampling of some population groups. Manual case-by-case revision has been commonly applied in order to identify and correct potential errors and omissions such as omitted or misreported assets, income and debts. We derive a machine learning approach for the purpose of classifying survey data affected by severe errors and omissions in the revision phase. Using data from the Spanish Survey of Household Finances we provide the best-performing supervised classification al
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