Author:
Chen Hong,Hu Songhua,Hua Rui,Zhao Xiuju
Abstract
AbstractNaive Bayesian classification algorithm is widely used in big data analysis and other fields because of its simple and fast algorithm structure. Aiming at the shortcomings of the naive Bayes classification algorithm, this paper uses feature weighting and Laplace calibration to improve it, and obtains the improved naive Bayes classification algorithm. Through numerical simulation, it is found that when the sample size is large, the accuracy of the improved naive Bayes classification algorithm is more than 99%, and it is very stable; when the sample attribute is less than 400 and the number of categories is less than 24, the accuracy of the improved naive Bayes classification algorithm is more than 95%. Through empirical research, it is found that the improved naive Bayes classification algorithm can greatly improve the correct rate of discrimination analysis from 49.5 to 92%. Through robustness analysis, the improved naive Bayes classification algorithm has higher accuracy.
Publisher
Springer Science and Business Media LLC
Reference35 articles.
1. H. Shakir, H. Rasheed, T.M.R. Khan, Radiomic feature selection for lung cancer classifiers [J]. J. Intell. Fuzzy Syst. 38(5), 1–9 (2020)
2. B. Ehsani-Moghaddam, J.A. Queenan, J. Mackenzie, et al., Mucopolysaccharidosis type II detection by naïve Bayes classifier: an example of patient classification for a rare disease using electronic medical records from the Canadian Primary Care Sentinel Surveillance Network [J]. PLoS One 13(12), 251–265 (2018)
3. H. Zhang, L. Ding, Y. Zou, et al., Predicting drug-induced liver injury in human with naïve Bayes classifier approach [J]. J. Comput. Aided Mol. Des. 30(10), 889–898 (2016)
4. S.C. Chu, T.K. Dao, J.S. Pan, et al., Identifying correctness data scheme for aggregating data in cluster heads of wireless sensor network based on naive Bayes classification [J]. EURASIP J. Wirel. Commun. Netw. 20(1), 963–982 (2020)
5. R. Rajalakshmi, C. Aravindan, A Naive Bayes approach for URL classification with supervised feature selection and rejection framework [J]. Comput. Intell. 34(1), 363–396 (2018)
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