Incremental Learning by Heterogeneous Bagging Ensemble

摘要：

Classifier ensemble is a main direction of incremental learning researches, and many ensemblebased incremental learning methods have been presented. Among them, Learn++, which is derived from the famous ensemble algorithm, AdaBoost, is special. Learn++ can work with any type of classifiers, either they are specially designed for incremental learning or not, this makes Learn++ potentially supports heterogeneous base classifiers. Based on massive experiments we analyze the advantages and disadvantages of Learn++. Then a new ensemble incremental learning method, Bagging++, is presented, which is based on another famous ensemble method: Bagging. The experimental results show that Bagging ensemble is a promising method for incremental learning and heterogeneous Bagging++ has the better generalization and learning speed than other compared methods such as Learn++ and NCL.

关键词： Incremental Learning Ensemble learning Learn++, Bagging

作者: Qiang Li Zhao Yan Huang Jiang Ming Xu

作者单位: School of Computer Science, National University of Defense Technology,Changsha 410073, China

会议类型: 国际会议

会议名称: 6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)

会议地点: 重庆

会议语种:英文

页码: 1-12

在线出版日期: 2010-11-19（万方平台首次上网日期，不代表论文的发表时间）