Incremental Learning Based on Ensemble Pruning

摘要：

Bagging, a widely used ensemble method, is simple and fast, and can generate heterogeneous base classifiers. This research proposes an incremental learning algorithm, PBagging++, based on ensemble pruning. In the algorithm, Bagging is adopted to generate a set of heterogeneous classifiers for each incremental data set. Then an ensemble pruning method is used to select base classifiers from the generated ones and add them to the target ensemble. The new target ensemble will perform the prediction on new instances. Experimental results show that ensemble pruning is an effective way to improve the predictive performance for ensemble based incremental learning.

关键词： incremental learning ensemble pruning bagging PBagging++

作者: Qiang-Li Zhao Yan-Huang Jiang Ming Xu

作者单位: School of Computer, National University of Defense Technology Changsha, Hunan Province, China

会议类型: 国际会议

会议名称: 2011 Eighth International Conference on Fuzzy System and Knowledge Discovery(第八届模糊系统与知识发现国际会议 FSKD 2011)

会议地点: 上海

会议语种:英文

页码: 381-385

在线出版日期: 2011-07-26（万方平台首次上网日期，不代表论文的发表时间）