基于Boosting的TAN组合分类器

Boosting-Based TAN Combination Classifier

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 TAN(tree augmentedna veBayes)是一种树状结构的贝叶斯网络 ,标准的TAN学习算法生成的TAN分类器是稳定的 ,用Boosting难以提高其分类性能 提出一种构造TAN的新算法GTAN ,并将由GTAN生成的多个TAN分类器用组合方法BoostingMultiTAN组合 ,最后实验比较了TAN组合分类器与标准的TAN分类器 实验结果表明 ,在大多数实验数据上 ,Boosting

Boosting is an effective classifier combination method, which can improve classification performance of an unstable learning algorithm. But it does not make much more improvement of a stable learning algorithm. TAN, tree-augmented nave Bayes, is a tree-like Bayesian network. The standard TAN learning algorithm generates a stable TAN classifier, whose accuracy is difficult to improve by the Boosting technique. In this paper, a new TAN learning algorithm called GTAN is presented, and multiple TAN classifiers generated by GTAN are combined by a combination method called Boosting-MultiTAN. Finally, this TAN combination classifier is compared with the standard TAN classifier by the experiments. Experimental results show that the Boosting MultiTAN has higher classification accuracy than the standard TAN classifier on most data sets.