Abstract:Naive Bayes Classification is simple and effective, but its strong attribute independency assumption limits its application scope. Concerning this problem, an improved WNBC algorithm is proposed based on attribute selection. This algorithm combines CFS algorithm with WNBC algorithm, it firstly uses CFS algorithm to get an attribute subset so that the simplified attribute subset can meet conditional independency; meanwhile, the algorithm's weighting coefficient is designed on that different attribute values have different influences on the classification result. Finally, it uses ASWNBC algorithm to classify datasets. The experimental results show that the proposed algorithm improves the classification accuracy with lower time consumption, therefore heightens the performance of NBC algorithm.