重庆邮电大学学报（自然科学版）2008，Vol.20Issue(3)：372-378,7.
A rough sets based pruning method for bagging ensemble
A rough sets based pruning method for bagging ensemble
摘要
Abstract
Ensemble techniques train a set of component classifiers and then combine their predictions to classify new pat-terns. Bagging is one of the most popular ensemble techniques for improving weak classifiers. However, it is hard to deployin many real applications because of the large memory requirement and high computation cost to store and vote the predic- tions of component classifiers. Rough set theory is a formal mathematical tool to deal with incomplete or imprecise informa- tion, which has attracted a lot of attention from theory and application fields. In this paper, a novel rough sets based meth-od is proposed to prune the classifiers obtained from bagging ensemble and select a subset of the component classifiers for aggregation. Experiment results show that the proposed method not only decreases the number of component classifiers but also obtains acceptable performance.关键词
Rough sets/ Bagging ensemble/ Pruning methodKey words
Rough sets/ Bagging ensemble/ Pruning method分类
信息技术与安全科学引用本文复制引用
MIAO Duo-qian ,WANG Rui-zhi,DUAN Qi-guo,LIU Ji-ming..A rough sets based pruning method for bagging ensemble[J].重庆邮电大学学报（自然科学版）,2008,20(3):372-378,7.基金项目
Supported by the National Natural Science Foundation of China (Granted No.670775036 abd No.60475019) and the Ph.D. program Foundation of Ministry of Education of China(No.20060247039) （Granted No.670775036 abd No.60475019）
