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- W2085169625 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." @default.
- W2085169625 created "2016-06-24" @default.
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- W2085169625 date "2010-01-01" @default.
- W2085169625 modified "2023-09-26" @default.
- W2085169625 title "Experiments with Two New Boosting Algorithms" @default.
- W2085169625 cites W2014129089 @default.
- W2085169625 doi "https://doi.org/10.4236/iim.2010.26047" @default.
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