Optimization of Bayesian Classifier Based on Flower Pollination Algorithm

This paper, the flower pollination algorithm(FPA) is adopted to optimize Naive Bayes classifier, and the Naive Bayesian classifier algorithm based on improved flower pollination algorithm(NBC-IFPA) is proposed. Firstly, the blacklist mechanism is introduced to make the FPA jump out of the local optimal solution. Secondly, the random perturbation term is introduced to increase the diversity of the population and improve the searching ability of FPA. Finally, the improved FPA is used to search for the global optimal attribute weights and use them into the weighted naive Bayesian model for classification. The simulation results show that the NBC-IFPA algorithm has higher classification accuracy.