Improved incremental algorithm of Naive Bayes - Shui-fei ZENG; Xiao-yan ZHANG; Xiao-feng DU; Tian-bo LU (Tongxin xuebao)
A novel Naive Bayes incremental algorithm was proposed,which could select new features.For the incremental sample selection of the unlabeled corpus,a minimum posterior probability was designed as the double threshold of sample selection by using the traditional class confidence.When new feature was detected in the corpus,it would be mapped into feature space,and then the corresponding classifier was updated.Thus this method played a very important role in class confidence threshold.Finally,it took advantage of the unlabeled and annotated corpus to validate improved incremental algorithm of Naive Bayes.The experimental results show that an improved incremental algorithm of Naive Bayes significantly outperforms traditonal incremental algorithm.