The paper "A survey on ensemble learning" by Xibin Dong, Zhiwen Yu, Wenming Cao, Yifan Shi, and Qianli Ma reviews the advancements and challenges in ensemble learning, a method that integrates multiple machine learning algorithms to improve predictive performance. Traditional machine learning methods often struggle with complex data, such as imbalanced, high-dimensional, and noisy datasets, due to their inability to capture multiple characteristics and underlying structures. Ensemble learning addresses this by combining diverse feature transformations and multiple learning algorithms to produce weak predictive results, which are then fused using voting schemes to achieve better performance. The authors classify ensemble learning approaches into four main categories: supervised ensemble classification, semi-supervised ensemble classification, clustering ensemble, and semi-supervised clustering ensemble. They discuss the research progress, algorithm applications, and challenges for each category, providing a comprehensive overview of the field. The paper also explores the integration of ensemble learning with other machine learning techniques, such as deep learning and reinforcement learning, and highlights potential future research directions. The introduction explains the concept of ensemble learning, emphasizing the balance between bias and variance in model complexity. The paper traces the historical development of ensemble learning, from early works by Dasarathy and Sheela to more recent advancements. It also includes figures illustrating the relationship between learning curves and model complexity, as well as the main research issues in ensemble classification and clustering. The subsequent sections of the paper delve into specific ensemble classification methods, including bagging, AdaBoost, random forest, random subspace, and gradient boosting, detailing their mechanisms and applications. The authors conclude with a summary and discussion, providing insights into the future of ensemble learning and its potential in addressing complex data challenges.The paper "A survey on ensemble learning" by Xibin Dong, Zhiwen Yu, Wenming Cao, Yifan Shi, and Qianli Ma reviews the advancements and challenges in ensemble learning, a method that integrates multiple machine learning algorithms to improve predictive performance. Traditional machine learning methods often struggle with complex data, such as imbalanced, high-dimensional, and noisy datasets, due to their inability to capture multiple characteristics and underlying structures. Ensemble learning addresses this by combining diverse feature transformations and multiple learning algorithms to produce weak predictive results, which are then fused using voting schemes to achieve better performance. The authors classify ensemble learning approaches into four main categories: supervised ensemble classification, semi-supervised ensemble classification, clustering ensemble, and semi-supervised clustering ensemble. They discuss the research progress, algorithm applications, and challenges for each category, providing a comprehensive overview of the field. The paper also explores the integration of ensemble learning with other machine learning techniques, such as deep learning and reinforcement learning, and highlights potential future research directions. The introduction explains the concept of ensemble learning, emphasizing the balance between bias and variance in model complexity. The paper traces the historical development of ensemble learning, from early works by Dasarathy and Sheela to more recent advancements. It also includes figures illustrating the relationship between learning curves and model complexity, as well as the main research issues in ensemble classification and clustering. The subsequent sections of the paper delve into specific ensemble classification methods, including bagging, AdaBoost, random forest, random subspace, and gradient boosting, detailing their mechanisms and applications. The authors conclude with a summary and discussion, providing insights into the future of ensemble learning and its potential in addressing complex data challenges.