摘要： 提出一种应用于回归问题，以分类回归树为基学习器，并综合Boosting和Bagging算法的特点，利用变相似度聚类技术和贪婪算法来进行选择性集成学习的算法——SER-BagBoosting Trees算法。将其与几种常用的机器学习算法进行比较研究，得出该算法往往比其他集成学习算法具有更好的泛化性能和更高的运行效率。

关键词: 分类回归树, 自助抽样, 选择性集成

Abstract: This paper introduces a new ensemble algorithm, SER-BagBoosting Trees ensemble algorithm, which is a combination of tree predictors and is based on variational similarity cluster technology and greedy method, and it is combined with the features of Boosting and Bagging. Compared with a series of other learning algorithms, it often has better generalization ability and higher running efficiency.

Key words: Classification and Regression Trees(CART), bootstrap, selective ensemble

中图分类号: 

• TP301.6