Research of optimization to solve nonlinear equation based on granular computing
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-dons of component classifiers. Rough set theory is a fortnal mathematical tool to deal with incomplete or imprecise informa-lion, which bas 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 foraggregation. Experiment results show that the proposed method not only decreases the number of component classifiers butalso obtains acceptable performance.
Rough sets Bagging ensemble Pruning method
MIAO Duo-qian WANG Rui-zhi DUAN Qi-guo LIU Ji-ming
Department of Computer Science and Technology, Tongji University, Shanghai 201804, P. R. China Computer Science Department, Hung Kong Baptist University, Kowloon Tong, Hung Kong SAR
国内会议
重庆
英文
372-378
2008-05-13（万方平台首次上网日期，不代表论文的发表时间）