Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)

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Boosting Accuracy: Advanced Ensemble Learning Strategies

Authors
Gagandeep Marken1, Sofiur Rahaman2, *
1Assistant Prof., Department of Computer Science Engineering, Chandigarh University, Gharuan, India
2M.Sc. Data Science, Department of Mathematics, Chandigarh University, Gharuan, India
*Corresponding author. Email: sksofiurrahaman781@gmail.com
Corresponding Author
Sofiur Rahaman
Available Online 26 May 2025.
DOI
10.2991/978-94-6463-716-8_24How to use a DOI?
Keywords
GBM; Boosting; XG-Boost
Abstract

Determining how to improve machine learning’s predictive accuracy has led to notable developments in ensemble learning techniques. In order to solve prediction problems in a variety of datasets, this study, “Boosting Accuracy: Advanced Ensemble Learning Strategies,” explores and applies cutting-edge boosting techniques. Three well-known methods are specifically the subject of this study: Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Machines (GBM). The advantages and disadvantages of these approaches are carefully assessed over a range of forecast scenarios. Our proposal is to create hybrid models that combine boosting techniques with other machine learning algorithms to create strong ensemble frameworks that go beyond the limits of conventional boosting approaches. The goal of this hybridisation is to get better predictive performance and robustness by utilising the advantages of each distinct model.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
Series
Advances in Intelligent Systems Research
Publication Date
26 May 2025
ISBN
978-94-6463-716-8
ISSN
1951-6851
DOI
10.2991/978-94-6463-716-8_24How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Gagandeep Marken
AU  - Sofiur Rahaman
PY  - 2025
DA  - 2025/05/26
TI  - Boosting Accuracy: Advanced Ensemble Learning Strategies
BT  - Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
PB  - Atlantis Press
SP  - 287
EP  - 304
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-716-8_24
DO  - 10.2991/978-94-6463-716-8_24
ID  - Marken2025
ER  -