集成学习研究现状及展望

Research Status and Prospect of Ensemble Learning

集成学习是机器学习的重要研究内容.集成学习通过集成组合已有的机器学习模型,能够使得集成模型的性能超过其中任何的单个模型.从集成回归和集成分类两个方面,总结分析了集成学习有效性的理论依据;分析了提升集成学习多样性的方法;分析了bagging、boosting、stacking、多核学习、集成深度学习等集成学习方法的研究进展,并讨论了集成学习未来需要关注的重点问题.

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.