【摘要】 Boosting是一种有效的分类器组合方法,它能够提高不稳定学习算法的分类性能,但对稳定的学习算法效果不明显.BAN(BN augmented Naive-Bayes)是一种增强的贝叶斯网络分类器,通过Boosting很容易提高其分类性能.比较了GBN(general BN)和BAN的打包分类器Wrapping-BAN-GBN与基于Boosting的BAN组合分类器Boosting-BAN.最后通过实验结果显示了在大多数实验数据上,Boosting-BAN分类器显示出较高的分类正确率.更多还原

【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 on a stable learning algorithm.BAN,i.e.BN augmented Naive-Bayes,is an augmented Bayesian network classifier,whose accuracy is easy to improve by the Boosting technique.In this paper,a wrapping classifier which wraps around GBN and BAN is compared with the Boosting-BAN classifier which is Boosting based on BAN combination classifier.Finally,experimental results show that the Boosting-BAN has higher classification accuracy on most data sets.更多还原