Scaling Up the Accuracy of Decision-Tree Classifiers: A Naive-Bayes Combination
C4.5 and NB are two of the top 10 algorithms in data mining thanks to their simplicity, effectiveness, and efficiency. In order to integrate their advantages, NBTree builds a naive bayes classifier on each leaf node of the built decision tree. NBTree significantly outperforms C4.5 and NB in terms of classification accuracy. However, it incurs very high time complexity. In this paper, the authors propose a very simple, effective, and efficient algorithm based on C4.5 and NB. They simply denote it C4.5-NB. Their motivation is to keep the high classification accuracy of NBTree without incurring the high time complexity.