Relevant bibliographies by topics / Boosting Algorithm

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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 'Boosting Algorithm'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Boosting Algorithm"

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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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Li, Xiao Bo. "Contrast Research of Two Kinds of Integrated Sorting Algorithms." Advanced Materials Research 433-440 (January 2012): 4025–31. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.4025.

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Boosting and Bagging are two kinds of important voting sorting algorithms. Boosting algorithm can generate multiple classifiers by serialization through adjustment of sample weight; Bagging can generate multiple classifiers by parallelization. Different algorithms are composed of different loss and different integration mode, through integration of Bagging and Boosting algorithm and naïve Bayes algorithm, the Bagging NB and AdaBoost NB algorithms are constructed. Through experiment contrast of UCI data set, the result shows Bagging NB algorithm is relatively stable, it can produce the sorting
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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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Bayu, Suseno, Bagus Sartono, and Khairil Anwar Notodiputro. "Cost-Sensitive Boosting Algorithm for Classifying Underdeveloped Regions in Indonesia." Proceedings of The International Conference on Data Science and Official Statistics 2023, no. 1 (2023): 296–308. http://dx.doi.org/10.34123/icdsos.v2023i1.373.

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Imbalanced classes are indicated by having more instances of some classes than others. The cost-sensitive boosting algorithm is a modification of the AdaBoost algorithm, which aims to solve the problem of imbalanced classes. In this study, we evaluate the cost-sensitive Boosting algorithm AdaC2 using Indonesia's underdeveloped region's data. This study confirms that the cost-sensitive boosting algorithm (AdaC2) performs better in classifying the instances in the minority classes than standard classifiers algorithms.
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Fang, Zhuang, Xuming Yi, and Liming Tang. "An Adaptive Boosting Algorithm for Image Denoising." Mathematical Problems in Engineering 2019 (February 18, 2019): 1–14. http://dx.doi.org/10.1155/2019/8365932.

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Image denoising is an important problem in many fields of image processing. Boosting algorithm attracts extensive attention in recent years, which provides a general framework by strengthening the original noisy image. In such framework, many classical existing denoising algorithms can improve the denoising performance. However, the boosting step is fixed or nonadaptive; i.e., the noise level in iteration steps is set to be a constant. In this work, we propose a noise level estimation algorithm by combining the overestimation and underestimation results. Based on this, we further propose an ad
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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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Anita Desiani, Siti Nurhaliza, Tri Febriani Putri, and Bambang Suprihatin. "Algoritma Extreme Gradient Boosting (XGBoost) dan Adaptive Boosting (AdaBoost) Untuk Klasifikasi Penyakit Tiroid." Jurnal Rekayasa Elektro Sriwijaya 6, no. 2 (2025): 66–75. https://doi.org/10.36706/jres.v6i2.145.

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Thyroid disease is a disease of the thyroid gland that can interfere with daily activities. Early detection of thyroid disease can have an important impact in optimizing the development of early detection systems that are more effective and accurate in detecting the disease. Data mining approaches can be used to solve this problem by utilizing various available algorithms, such as Adaptive Boosting and Extreme Gradient Boosting. This research aims to improve the development of early thyroid disease prediction by comparing the two algorithms by utilizing the percentage split method. This resear
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Meng, Xiyan, and Fang Zhuang. "A New Boosting Algorithm for Shrinkage Curve Learning." Mathematical Problems in Engineering 2022 (April 15, 2022): 1–14. http://dx.doi.org/10.1155/2022/6339758.

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To a large extent, classical boosting denoising algorithms can improve denoising performance. However, these algorithms can only work well when the denoisers are linear. In this paper, we propose a boosting algorithm that can be used for a nonlinear denoiser. We further implement the proposed algorithm into a shrinkage curve learning denoising algorithm, which is a nonlinear denoiser. Concurrently, the convergence of the proposed algorithm is proved. Experimental results indicate that the proposed algorithm is effective and the dependence of the shrinkage curve learning denoising algorithm on
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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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Dissertations / Theses on the topic "Boosting Algorithm"

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Iyer, Raj Dharmarajan 1976. "An efficient boosting algorithm for combining preferences." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80203.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.<br>Includes bibliographical references (p. 79-84).<br>by Raj Dharmarajan Iyer, Jr.<br>S.M.
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DUARTE, JULIO CESAR. "THE BOOSTING AT START ALGORITHM AND ITS APPLICATIONS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=31451@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>INSTITUTO MILITAR DE ENGENHARIA<br>CENTRO TECNOLÓGICO DO EXÉRCITO<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>Boosting é uma técnica de aprendizado de máquina que combina diversos classificadores fracos com o objetivo de melhorar a acurácia geral. Em cada iteração, o algoritmo atualiza os pesos dos exemplos e constrói um classificador adicional. Um esquema simples de votação é utilizado para combinar os classificadores. O algoritmo mais famoso baseado em Boosting é o AdaBoost. Este
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Pratap, Amrit Abu-Mostafa Yaser S. "Maximum drawdown of a Brownian motion and AlphaBoost : a boosting algorithm /." Diss., Pasadena, Calif. : California Institute of Technology, 2004. http://resolver.caltech.edu/CaltechETD:etd-05272004-115820.

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Kepe, Tiago Rodrigo. "A tuning approach based on evolutionary algorithm and data sampling for boosting performance of mapreduce programs." reponame:Repositório Institucional da UFPR, 2013. http://hdl.handle.net/1884/36783.

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Orientador : Prof. Dr. Eduardo C. de Almeida<br>Dissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 25/08/2014<br>Inclui referências<br>Resumo: O software de processamento de dados Apache Hadoop está introduzido em um ambiente complexo composto de enormes cluster de máquinas, grandes conjuntos de dados e vários programas de processamento. Administrar tal ambiente demanda tempo, é dispendioso e requer usuários experts. Por isso, falta de conhecimento pode ocasionar falhas de configurações degradando a per
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Neo, Toh Koon Charlie. "A direct boosting algorithm for the k-nearest neighbor classifier via local warping of the distance metric /." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2168.pdf.

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Neo, TohKoon. "A Direct Algorithm for the K-Nearest-Neighbor Classifier via Local Warping of the Distance Metric." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2168.pdf.

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Xia, Tian. "Learning to Rank Algorithms and Their Application in Machine Translation." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1451610555.

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Piro, Paolo. "Learning prototype-based classification rules in a boosting framework: application to real-world and medical image categorization." Phd thesis, Université de Nice Sophia-Antipolis, 2010. http://tel.archives-ouvertes.fr/tel-00590403.

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Thompson, Simon Giles. "Distributed boosting algorithms." Thesis, University of Portsmouth, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285529.

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Тимчак, Олександра Ігорівна, та Oleksandra Tymchak. "Алгоритм розпізнавання особи за зображенням обличчя для охоронних систем контролю та безпеки". Master's thesis, Тернопільський національний технічний університет імені Івана Пулюя, 2021. http://elartu.tntu.edu.ua/handle/lib/36674.

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У кваліфікаційній роботі розглянуто сучасні системи безпеки, в яких використовуються модулі розпізнавання обличчя. Проаналізовано загальні недоліки та фактори, що впливають на ефективність роботи. Проведено порівняльний аналіз існуючих алгоритмів і методів виділення та розпізнавання обличчя на зображеннях та запропоновано алгоритм попередньої обробки, який може підвищити ймовірність правильного виділення та подальшого розпізнавання обличчя на зображенні.<br>The qualification work considers modern security systems that use face recognition modules. The general shortcomings and factors influenci
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Books on the topic "Boosting Algorithm"

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Thompson, Simon Giles. Distributed boosting algorithms. University of Portsmouth, 1999.

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Schapire, Robert E. Boosting: Foundations and algorithms. MIT Press, 2012.

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Bach, Francis, Yoav Freund, and Robert E. Schapire. Boosting: Foundations and Algorithms. MIT Press, 2012.

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Bach, Francis, Yoav Freund, and Robert E. Schapire. Boosting: Foundations and Algorithms. MIT Press, 2014.

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Freund, Yoav, and Robert E. Schapire. Boosting: Foundations and Algorithms. MIT Press, 2018.

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Tree-based Machine Learning Algorithms: Decision Trees, Random Forests, and Boosting. CreateSpace Independent Publishing Platform, 2017.

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Book chapters on the topic "Boosting Algorithm"

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Nock, Richard, and Patrice Lefaucheur. "A Robust Boosting Algorithm." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36755-1_27.

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Bertoni, A., P. Campadelli, and M. Parodi. "A boosting algorithm for regression." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0020178.

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Esposito, Roberto, and Lorenza Saitta. "Boosting as a Monte Carlo Algorithm." In AI*IA 2001: Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45411-x_2.

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Mitsuboshi, Ryotaro, Kohei Hatano, and Eiji Takimoto. "Soft Margin Boosting as Frank-Wolfe Algorithms." In Algorithmic Foundations for Social Advancement. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-0668-9_21.

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Abstract We consider the LPBoost family of boosting algorithms for the $$\ell _1$$ ℓ 1 -norm regularized soft margin optimization, where the problem instance is implicitly given as a huge scale LP problem and the goal is to efficiently find an optimal solution with the aid of a certain oracle. Although the optimal solution yields a linear classifier with good generalization ability, the LPBoost family is less popular, since all existing algorithms in the family are either very slow on real data or have no theoretical convergence guarantees. In this chapter, we first show that each algorithm in
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Leyton-Brown, Kevin, Eugene Nudelman, Galen Andrew, Jim McFadden, and Yoav Shoham. "Boosting as a Metaphor for Algorithm Design." In Principles and Practice of Constraint Programming – CP 2003. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45193-8_75.

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Nizamani, Sarwat, Nasrullah Memon, and Uffe Kock Wiil. "Detection of Illegitimate Emails Using Boosting Algorithm." In Lecture Notes in Social Networks. Springer Vienna, 2011. http://dx.doi.org/10.1007/978-3-7091-0388-3_13.

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Kaur, Harpreet, Shruti Bhargava Choubey, Abhishek Choubey, K. Sai Deekshith, B. Veeranna, and Y. Santhosh Reddy. "Detection of Arrhythmia Using Adaptive Boosting Algorithm." In Lecture Notes in Networks and Systems. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8512-5_31.

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Leskes, Boaz. "The Value of Agreement, a New Boosting Algorithm." In Learning Theory. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11503415_7.

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Rajakumari, P. Anitha, and Pritee Parwekar. "Boosting Blockchain Mechanism Using Cryptographic Algorithm in WSN." In Rising Threats in Expert Applications and Solutions. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1122-4_53.

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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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Conference papers on the topic "Boosting Algorithm"

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Patnaik, Archana, Neelamadhab Padhy, Lov Kumar, and Rasmita Panigrahi. "Metrics-Based Refactoring Prediction Using Boosting Algorithm." In 2025 International Conference on Emerging Systems and Intelligent Computing (ESIC). IEEE, 2025. https://doi.org/10.1109/esic64052.2025.10962571.

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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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Agarwal, Shweta, Bobbinpreet Kaur, and Bhoopesh Singh Bhati. "Boosting Feature Selection Using Modified Grasshopper Algorithm: Emphasizing Social Interaction." In 2024 International Conference on Artificial Intelligence and Emerging Technology (Global AI Summit). IEEE, 2024. https://doi.org/10.1109/globalaisummit62156.2024.10947963.

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Semitra, Dhea Salsabila, Riska Yanu Fa'rifah, and Dita Pramesti. "Clinical Data-Driven Prediction of Pulmonary Tuberculosis with Comorbidities Using Extreme Gradient Boosting (XGBoost)." In 2025 4th International Conference on Electronics Representation and Algorithm (ICERA). IEEE, 2025. https://doi.org/10.1109/icera66156.2025.11087377.

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Phoungphol, Piyaphol, and Inthira Srivrunyoo. "Boosting-genetic clustering algorithm." In 2012 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2012. http://dx.doi.org/10.1109/icmlc.2012.6359529.

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Lazarevic, Aleksandar, and Zoran Obradovic. "The distributed boosting algorithm." In the seventh ACM SIGKDD international conference. ACM Press, 2001. http://dx.doi.org/10.1145/502512.502557.

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Reports on the topic "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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