The Boosting-based BAN Combination Classifier
LI Xiao-yi · Shuxue de shijian yu renshi · 2009
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.