An improved TANC classification algorithm based on C4.5
Xiaoqiang Zhao, Yang Jia-min · 2014
Tree Augmented Naive Bayes Classification (TANC) is not very well to deal with continuous data and it ignores partial data in the absence of data attribute value and this can reduce the result accuracy. To resolve this problem, an improved algorithm based on C4.5 is proposed in this paper. The proposed algorithm firstly modifies the available training data according to the predictions of C4.5, then continuous data is discretized by dividing many finite intervals of attributes, this modified training data is used to train TANC. In this way it can improve the classification accuracy of the TANC. The experimental results show that the improved algorithm is superior to TANC in terms of classification accuracy.