A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

Ensemble learning techniques have achieved advanced performance in diverse machine learning applications by combining the predictions from two or more base models. The study focuses on the widely used ensemble algorithms, including random forest, adaptive boosting (AdaBoost), gradient boosting, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost).

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• 1 This paper presents a concise overview of ensemble learning, covering the three main ensemble methods: bagging, boosting, and stacking, their early development to the recent advanced algorithms.
• 2 The study focuses on the widely used ensemble algorithms, including random forest, adaptive boosting (AdaBoost), gradient boosting, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost).
• 3 An attempt is made to concisely cover their mathematical and algorithmic representations, which is lacking in the existing literature and would be beneficial to machine learning researchers and practitioners.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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