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Relevant bibliographies by topics / Gradient Boosting Algorithm

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Academic literature on the topic 'Gradient Boosting Algorithm'

Author: Grafiati

Published: 7 June 2025

Last updated: 17 July 2025

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Journal articles on the topic "Gradient Boosting Algorithm"

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Hosen, Md Saikat, and Ruhul Amin. "Significant of Gradient Boosting Algorithm in Data Management System." Engineering International 9, no. 2 (2021): 85–100. http://dx.doi.org/10.18034/ei.v9i2.559.

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Gradient boosting machines, the learning process successively fits fresh prototypes to offer a more precise approximation of the response parameter. The principle notion associated with this algorithm is that a fresh base-learner construct to be extremely correlated with the “negative gradient of the loss function” related to the entire ensemble. The loss function's usefulness can be random, nonetheless, for a clearer understanding of this subject, if the “error function is the model squared-error loss”, then the learning process would end up in sequential error-fitting. This study is aimed at

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Kumar, H. Kishore, and S. Ashok Kumar. "Accurate Biometric Palm Print Recognition Using ResNet50 algorithm Over X Gradient Boosting Algorithm." E3S Web of Conferences 399 (2023): 04027. http://dx.doi.org/10.1051/e3sconf/202339904027.

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The aim of this research is to enhance the accuracy of biometric palm print identification by using the Novel ResNet50 Algorithm as compared to the X Gradient Boosting. Materials and Methods: In this study, the ResNet50 and X Gradient Boosting algorithms were compared using a sample size of 10 for each algorithm, resulting in a total sample size of 20. The comparison was carried out with a G Power of 0.8 and a confidence interval (CI) of 95% to ensure statistical significance. For this study the Birjand University Mobile Palmprint Database (BMPD) dataset was collected from the Kaggle repositor

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Mohan, B. R., Dileep M, Vijay Bhuria, Sai Sudha Gadde, Kumarasamy M, and Achyutha Prasad N. "Potable Water Identification with Machine Learning: An Exploration of Water Quality Parameters." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 3 (2023): 178–85. http://dx.doi.org/10.17762/ijritcc.v11i3.6333.

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In this research, we aim to determine the water potability using three machine learning classification algorithms: decision tree, gradient boosting and bagging classifier. These algorithms were trained and tested on a dataset of water quality measurements. The outcomes of the experiment showed that the gradient boosting algorithm achieved the highest F1-score of 0.78 among all the algorithms. This indicates that the gradient boosting algorithm was most effective in correctly identifying both the safe and contaminated water samples. The results of this study demonstrate that gradient boosting i

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Kaushalya, Dissanayak, and Gapar Md Johar Md. "Two-level boosting classifiers ensemble based on feature selection for heart disease prediction." Two-level boosting classifiers ensemble based on feature selection for heart disease prediction 32, no. 1 (2023): 381–91. https://doi.org/10.11591/ijeecs.v32.i1.pp381-391.

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Heart disease is a prevalent global health concern, necessitating early detection to save lives. Machine learning has revolutionized medical research, prompting the investigation of boosting algorithms for heart disease prediction. This study employs three heart disease datasets from the University of California Irvine (UCI) repository: Cleveland, Statlog, and Long Beach, with 14 features each. Recursive feature elimination with a support vector machine (SVM) is utilized to identify significant features. Five boosting algorithms (gradient boosting algorithm (GB), adaptive boosting algorithms (

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Dissanyake, Kaushalya, and Md Gapar Md Johar. "Two-level boosting classifiers ensemble based on feature selection for heart disease prediction." Indonesian Journal of Electrical Engineering and Computer Science 32, no. 1 (2023): 381. http://dx.doi.org/10.11591/ijeecs.v32.i1.pp381-391.

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<span>Heart disease is a prevalent global health concern, necessitating early detection to save lives. Machine learning has revolutionized medical research, prompting the investigation of boosting algorithms for heart disease prediction. This study employs three heart disease datasets from the University of California Irvine (UCI) repository: Cleveland, Statlog, and Long Beach, with 14 features each. Recursive feature elimination with a support vector machine (SVM) is utilized to identify significant features. Five boosting algorithms (gradient boosting algorithm (GB), adaptive boosting

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Adnan, A., A. M. Yolanda, and F. Natasya. "A Comparison of Bagging and Boosting on Classification Data: Case Study on Rainfall Data in Sultan Syarif Kasim II Meteorological Station in Pekanbaru." Journal of Physics: Conference Series 2049, no. 1 (2021): 012053. http://dx.doi.org/10.1088/1742-6596/2049/1/012053.

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Abstract A frequent way for classification data is using a machine learning algorithm alongside ensemble methods like bagging and boosting. In earlier studies, these two algorithms have shown to be very accurate. The aim of this research is to discover performance of bagging and boosting to classify rainfall data obtained at the Sultan Syarif Kasim II Meteorological Station in Pekanbaru from 1 January 2018 until 31 July 2021. Rainfall data are classified into two categories: rainy and non-rainy. The parameters are average temperature, average humidity, sunshine duration, wind direction at maxi

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Zhao, Changyuan, Hongyang Du, Guangyuan Liu, and Dusit Niyato. "Supervised Score-Based Modeling by Gradient Boosting." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 21 (2025): 22768–76. https://doi.org/10.1609/aaai.v39i21.34437.

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Score-based generative models can effectively learn the distribution of data by estimating the gradient of the distribution. Due to the multi-step denoising characteristic, researchers have recently considered combining score-based generative models with the gradient boosting algorithm, a multi-step supervised learning algorithm, to solve supervised learning tasks. However, existing generative model algorithms are often limited by the stochastic nature of the models and the long inference time, impacting prediction performances. Therefore, we propose a Supervised Score-based Model (SSM), which

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Fan, Bo, Jianbing Zhang, and Ganglong Fan. "Recommendation Algorithm-Driven Product Popularity Prediction: A Data Analytics Perspective." Insights in Computer, Signals and Systems 1, no. 1 (2024): 20–33. http://dx.doi.org/10.70088/2kc99z38.

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This paper explores the optimization of recommendation systems using gradient boosting machine learning models. Traditional recommendation algorithms, such as collaborative filtering, often struggle with sparsity and cold start problems. Gradient boosting offers a robust alternative, capable of capturing complex interactions between users and items while handling both categorical and numerical data effectively. This study examines the theoretical foundations of gradient boosting and discusses optimization techniques, including regularization, hyperparameter tuning, and ensembling, that enhance

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Jinan, Abwabul, Zakarias Situmorang, and Rika Rosnelly. "Bulldog Breed Classification Using VGG-19 and Ensemble Learning." International Conference on Information Science and Technology Innovation (ICoSTEC) 2, no. 1 (2023): 29–33. http://dx.doi.org/10.35842/icostec.v2i1.32.

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In image classification, the C4.5, Adaboost, and Gradient Boosting algorithms need another method to extract the image's features in the classification process. This research employs transfer learning with the VGG-19 network for the image's features extraction and transfers the result as a dataset to classify image-based Bulldog breeds. As the classifier to classify the extracted features from the VGG 16 model, we employ three ensemble learning algorithms, namely C4.5, AdaBoost, and Gradient Boost. The training data classification results of the American, English, and French bulldog breeds sho

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Purba, Windania, Sumita Wardani, Diana Febrina Lumbantoruan, Fransiska Celia Ivoi Silalahi, and Thomas Leo Edison. "OPTIMIZATION OF LUNG CANCER CLASSIFICATION METHOD USING EDA-BASED MACHINE LEARNING." Jurnal Sistem Informasi dan Ilmu Komputer Prima(JUSIKOM PRIMA) 6, no. 2 (2023): 43–50. http://dx.doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v6i2.3413.

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Lung cancer is one of the three deadliest diseases in the world and has rapidly developed. Based on this, researchers conducted research to predict the factors that influence lung cancer. One method to identify this is using data mining methods and classification techniques. Researchers used several popular algorithms in classification to make comparisons of the most accurate algorithms for lung cancer classification. The algorithms used include K-Nearest Neighbor, Random Forest Classifier, Logistic Regression, Linear SVM, Naïve Bayes, Decision Tree, Random Forest, Gradient Boosting, Kernel SV

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Dissertations / Theses on the topic "Gradient Boosting Algorithm"

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Mayr, Andreas [Verfasser]. "Boosting beyond the mean - extending component-wise gradient boosting algorithms to multiple dimensions / Andreas Mayr." München : Verlag Dr. Hut, 2013. http://d-nb.info/104287848X/34.

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Sjöblom, Niklas. "Evolutionary algorithms in statistical learning : Automating the optimization procedure." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160118.

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Scania has been working with statistics for a long time but has invested in becoming a data driven company more recently and uses data science in almost all business functions. The algorithms developed by the data scientists need to be optimized to be fully utilized and traditionally this is a manual and time consuming process. What this thesis investigates is if and how well evolutionary algorithms can be used to automate the optimization process. The evaluation was done by implementing and analyzing four variations of genetic algorithms with different levels of complexity and tuning paramete

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Mayrink, Victor Teixeira de Melo. "Avaliação do algoritmo Gradient Boosting em aplicações de previsão de carga elétrica a curto prazo." Universidade Federal de Juiz de Fora (UFJF), 2016. https://repositorio.ufjf.br/jspui/handle/ufjf/3563.

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Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-03-07T14:25:21Z No. of bitstreams: 1 victorteixeirademelomayrink.pdf: 2587774 bytes, checksum: 1319cc37a15480796050b618b4d7e5f7 (MD5)<br>Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-03-07T15:06:57Z (GMT) No. of bitstreams: 1 victorteixeirademelomayrink.pdf: 2587774 bytes, checksum: 1319cc37a15480796050b618b4d7e5f7 (MD5)<br>Made available in DSpace on 2017-03-07T15:06:57Z (GMT). No. of bitstreams: 1 victorteixeirademelomayrink.pdf: 2587774 bytes, checksum: 1319cc37a15480796050b618b4d7e5f7

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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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Hepp, Tobias [Verfasser], Olaf [Akademischer Betreuer] Gefeller, Olaf [Gutachter] Gefeller, Andreas [Gutachter] Mayr, Mark [Gutachter] Stemmler, and Werner [Gutachter] Adler. "Erweiterung inferenzstatistischer Fähigkeiten modellbasierter Gradient-Boosting Algorithmen / Tobias Hepp ; Gutachter: Olaf Gefeller, Andreas Mayr, Mark Stemmler, Werner Adler ; Betreuer: Olaf Gefeller." Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2019. http://d-nb.info/1193729297/34.

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Camacho, Cosio Hernán. "Método de estabilidad para el dimensionamiento de tajeos obtenido mediante el algoritmo Gradient Boosting Machine considerando la incorporación de los esfuerzos activos en minería subterránea." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2020. http://hdl.handle.net/10757/656716.

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En las últimas cuatro décadas, el método gráfico de estabilidad de Mathews ha constituido el abanico de herramientas indispensables para el dimensionamiento de tajeos; caracterizándose por su eficiencia en costos, ahorro de tiempo y esfuerzo. Asimismo, el aporte de diversos autores por optimizar su rendimiento ha permitido desplegar una serie de criterios que han permitido abordar cada vez más escenarios. No obstante, con la diversificación de la minería en diferentes contextos geológicos y la necesidad trabajar a profundidades más altas se ha mostrado que el método gráfico de estabilidad ha d

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Ломотин, К. Е. "Сравнение алгоритмов адаптивного и градиентного бустинга в задаче классификации текстов". Thesis, Сумский государственный университет, 2017. http://essuir.sumdu.edu.ua/handle/123456789/65585.

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Работа посвящена сравнению двух наиболее популярных алгоритмов бустинга: AdaBoost и градиентного бустинга в задаче классификации научных статей по рубрикам первого уровня УДК. Главное различие этих алгоритмов заключается в методе коррекции весовых коэффициентов и параметров базовых моделей, входящих в их состав.

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Sonnert, Adrian. "Predicting inter-frequency measurements in an LTE network using supervised machine learning : a comparative study of learning algorithms and data processing techniques." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148553.

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With increasing demands on network reliability and speed, network suppliers need to effectivize their communications algorithms. Frequency measurements are a core part of mobile network communications, increasing their effectiveness would increase the effectiveness of many network processes such as handovers, load balancing, and carrier aggregation. This study examines the possibility of using supervised learning to predict the signal of inter-frequency measurements by investigating various learning algorithms and pre-processing techniques. We found that random forests have the highest predict

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Liao, Wei-Chun, and 廖維君. "A Gradient Boosting Algorithm Based on Gaussian Process Regression." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/sa3vf5.

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碩士<br>國立臺灣大學<br>資訊管理學研究所<br>106<br>Gaussian process regression (GPR) is an important model in the field of machine learning. GPR model is flexible, robust, and easy to implement. However, it suffers from expensive computational cost: O(n^3) for training time, O(n^2) for training memory and O(n) for testing time, where n is the number of observations in training data. In this work, we develop a fast approximation method to reduce the time and space complexity. The proposed method is related to the design of gradient boosting algorithm. We conduct experiments using real-world dataset and demonst

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Bureš, Michal. "Strojové učení v algoritmickém obchodování." Master's thesis, 2021. http://www.nusl.cz/ntk/nusl-438032.

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This thesis is dedicated to the application of machine learning methods to algorithmic trading. We take inspiration from intraday traders and implement a system that predicts future price based on candlestick patterns and technical indicators. Using forex and US stocks tick data we create multiple aggregated bar representations. From these bars we construct original features based on candlestick pattern clustering by K-Means and long-term features derived from standard technical indicators. We then setup regression and classification tasks for Extreme Gradient Boosting models. From their predi

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Book chapters on the topic "Gradient Boosting Algorithm"

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Gupta, Neha, Vinita Jindal, and Punam Bedi. "Encrypted Traffic Classification Using eXtreme Gradient Boosting Algorithm." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3071-2_20.

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Gohil, Nayanaba Pravinsinh, and Arvind D. Meniya. "Click Ad Fraud Detection Using XGBoost Gradient Boosting Algorithm." In Communications in Computer and Information Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76776-1_5.

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Chittaragi, Nagaratna B., and Shashidhar G. Koolagudi. "Sentence-Based Dialect Identification System Using Extreme Gradient Boosting Algorithm." In Advances in Intelligent Systems and Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9683-0_14.

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Pham, Huu-Danh, Tuan Dinh Le, and Thanh Nguyen Vu. "Static PE Malware Detection Using Gradient Boosting Decision Trees Algorithm." In Future Data and Security Engineering. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03192-3_17.

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Gao, Kangkai, and Yong Wang. "A Novel Algorithm of Machine Learning: Fractional Gradient Boosting Decision Tree." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18123-8_58.

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Maaloul, Kamel, and Brahim Lejdel. "Big Data Analytics in Weather Forecasting Using Gradient Boosting Classifiers Algorithm." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4484-2_2.

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Deepa, S., and B. Booba. "Hybrid Ensemble Gradient Boosting Algorithm to Predict Diabetes Health Care Analytics." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86299-1_8.

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Kainthola, Ashutosh, Vishnu Himanshu Ratnam Pandey, and T. N. Singh. "Slime Mould Algorithm Enhanced Gradient Boosting Regressor for Prediction of Slope Stability." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-1757-6_19.

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Kumar, Pullela SVVSR, Praveen Neti, Dirisala J. Nagendra Kumar, G. S. N. Murthy, R. V. S. Lalitha, and Mylavarapu Kalyan Ram. "OXGBoost: An Optimized eXtreme Gradient Boosting Algorithm for Classification of Breast Cancer." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0840-8_4.

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Kanwar, Shailza, Lalit Kumar Awasthi, and Vivek Shrivastava. "Cross-Project Defect Prediction by Using Optimized Light Gradient Boosting Machine Algorithm." In Communication and Intelligent Systems. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2130-8_73.

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Conference papers on the topic "Gradient Boosting Algorithm"

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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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Priyanka, K., Saif Obaid, K. Anguraj, V. Thirumani Thangam, and R. Sahana. "Road Accident Detection using MobileNetv2 with Gradient Boosting Algorithm." In 2024 First International Conference on Software, Systems and Information Technology (SSITCON). IEEE, 2024. https://doi.org/10.1109/ssitcon62437.2024.10796061.

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Zhang, Hui, WeiWen Wu, Chunming Yang, and Bo Li. "A tree regression algorithm based on incremental gradient boosting." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650730.

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K, Balaabishek, and C. Bala Kamatchi. "Avoiding Clickbait in Social Media Using Gradient Boosting Algorithm." In 2025 International Conference on Inventive Computation Technologies (ICICT). IEEE, 2025. https://doi.org/10.1109/icict64420.2025.11005086.

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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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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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Ranganathan, Chitra Sabapathy, Ramesh S, J. Preetha, Nitin Rakesh, N. Mohankumar, and S. Sujatha. "IoT -Weather Integration for Enhanced Cricket Tactics with Gradient Boosting Algorithm." In 2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS). IEEE, 2024. https://doi.org/10.1109/csitss64042.2024.10816926.

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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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Anandhan, Logeswaran, Nandhini A., Sree Abinaya P., Ravikumar V., and Sabarish Sanjay. "Personalized Drug Information and Recommendation System Using Gradient Boosting Algorithm (GBM)." In 2024 13th International Conference on System Modeling & Advancement in Research Trends (SMART). IEEE, 2024. https://doi.org/10.1109/smart63812.2024.10882477.

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Wadhwa, Jasmeet Singh, Lavisha Jagwani, and B. Pitchaimanickam. "A Hybrid Gradient Boosting Algorithm for Dynamic Pricing using a Custom dataset." In 2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN). IEEE, 2024. http://dx.doi.org/10.1109/icipcn63822.2024.00043.

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Reports on the topic "Gradient Boosting Algorithm"

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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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Rossi, Jose Luiz, Carlos Piccioni, Marina Rossi, and Daniel Cuajeiro. Brazilian Exchange Rate Forecasting in High Frequency. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0004488.

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We investigated the predictability of the Brazilian exchange rate at High Frequency (1, 5 and 15 minutes), using local and global economic variables as predictors. In addition to the Linear Regression method, we use Machine Learning algorithms such as Ridge, Lasso, Elastic Net, Random Forest and Gradient Boosting. When considering contemporary predictors, it is possible to outperform the Random Walk at all frequencies, with local economic variables having greater predictive power than global ones. Machine Learning methods are also capable of reducing the mean squared error. When we consider on

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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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