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 IIM  Vol.2 No.6 , June 2010
Experiments with Two New Boosting Algorithms
Abstract: Boosting is an effective classifier combination method, which can improve classification performance of an unstable learning algorithm. But it dose not make much more improvement of a stable learning algorithm. In this paper, multiple TAN classifiers are combined by a combination method called Boosting-MultiTAN that is compared with the Boosting-BAN classifier which is boosting based on BAN combination. We describe experiments that carried out to assess how well the two algorithms perform on real learning problems. Fi- nally, experimental results show that the Boosting-BAN has higher classification accuracy on most data sets, but Boosting-MultiTAN has good effect on others. These results argue that boosting algorithm deserve more attention in machine learning and data mining communities.
Cite this paper: nullX. Sun and H. Zhou, "Experiments with Two New Boosting Algorithms," Intelligent Information Management, Vol. 2 No. 6, 2010, pp. 386-390. doi: 10.4236/iim.2010.26047.
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