A Combined Classification Algorithm Based on C4.5 and NB

Liangxiao Jiang, Chaoqun Li, Jia Wu, Jian Zhu

Abstract: When our learning task is to build a model with accurate classification, C4.5 and NB are two very important algorithms for achieving this task because of their simplicity and high performance. In this paper, we present a combined classification algorithm based on C4.5 and NB, simply C4.5-NB. In C4.5-NB, the class probability estimates of C4.5 and NB are weighted according to their classification accuracy on the training data. We experimentally tested C4.5-NB in Weka system using the whole 36 UCI data sets selected by Weka, and compared it with C4.5 and NB. The experimental results show that C4.5-NB significantly outperforms C4.5 and NB in terms of classification accuracy. Besides, we also observe the ranking performance of C4.5-NB in terms of AUC (the area under the Receiver Operating Characteristics curve). Fortunately, C4.5-NB also significantly outperforms C4.5 and NB.