Research on Naive Bayes Integration Method based on Kmeans++ digital teaching clustering
Tao Liu · 2023
Naive Bayes method is simple, efficient, accurate, and has a solid theoretical basis, has been widely used. In view of the difference is the key condition of integrated learning in digital teaching, this paper proposes a method to improve the integration difference of naive Bayes classifier based on Kmeans++ clustering technology, so as to improve the generalization performance of naive Bayes. Firstly, several naive Bayesky classifier models are developed through training of training samples. Then, in order to increase the difference between base classifiers, Kmemeans ++ algorithm is used to cluster the prediction results of 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. The method is verified by UCI standard data set, and the results show that the generalization performance of the method is greatly improved.