Experiments with Two New Boosting Algorithms
Xiaowei Sun, Hongbo Zhou
DOI: 10.4236/iim.2010.26047   PDF   HTML     6,457 Downloads   10,109 Views   Citations


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.

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X. 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.

Conflicts of Interest

The authors declare no conflicts of interest.


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