LUO Changwei 1, 2 WANG Shuangshuang1 YIN Junsong1 ZHU Siyu1 LIN Bo1 CAO Jiang1
1. War Research Institute, Academy of Military Science, Beijing 100091, China 2. Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Abstract：Ensemble learning is an important research content of machine learning. By integrating or combining existing machine learning models, ensemble learning can make the performance of the integrated model exceed any single model. The theory of the effectiveness of ensemble learning is summarized and analyzed from two aspects: ensemble regression and ensemble classification; the methods to improve the diversity of ensemble learning are analyzed; the research progress of the main ensemble learning methods such as bagging, boosting, stacking, multi-kernel learning and ensemble deep learning is analyzed; the key issues of ensemble learning to be focused in the future are discussed.
罗常伟, 王双双, 尹峻松, 朱思宇, 林波, 曹江. 集成学习研究现状及展望[J]. 指挥与控制学报, 2023, 9(1): 1-8.
LUO Changwei, WANG Shuangshuang, YIN Junsong, ZHU Siyu, LIN Bo, CAO Jiang. Research Status and Prospect of Ensemble Learning. Journal of Command and Control, 2023, 9(1): 1-8.
2023, Vol. 9 
