Abstract: Naive Bayes is widely applied because of its simple method,high computation efficiency,high accuracy and solid the oretical foundation.Since the difference is a key condition of ensemble learning,this paper studied the method for improving the ensemble difference of naive Bayes classifier based on kmeans++ clustering technology,so as to improve the generalization performance of naive Bayes.Firstly,plurality of naive Bayesian classifier models are trained through a training sample set.In order to increase the difference between the base classifiers,Kmeans++ algorithm is used to cluster the prediction results of the base classifiers on the verification set.Finally,the base classifier with the best generalization performance is selected from each cluster for ensemble learning,and the final result is obtained by simple voting method.UCI standard data sets are used to verify the algorithm at the end of this paper,and its generalization performance has been greatly improved.

Key words: Difference, Esemble learning, Kmeans++ clustering, Naive bayes