Improved naive Bayes classification algorithm for traffic risk management

Naive 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 obtain...

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Vydané v:EURASIP journal on advances in signal processing Ročník 2021; číslo 1; s. 1 - 12
Hlavní autori: Chen, Hong, Hu, Songhua, Hua, Rui, Zhao, Xiuju
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cham Springer International Publishing 22.06.2021
Springer
Springer Nature B.V
SpringerOpen
Predmet:
Accuracy
Algorithms
Classification
Data analysis
Discrimination analysis
Empirical analysis
Engineering
Feature weighted
Improved naive Bayesian classification algorithm
Information management
Intelligent Sensors trends in Real-time Signal Processing
Multivariate logistic regression
Numerical analysis
Quantum Information Technology
Risk management
Robustness (mathematics)
Signal,Image and Speech Processing
Spintronics
Traffic management
Traffic risk management
China
Improved naive Bayesian classification algorithm
Multivariate logistic regression
Feature weighted
Discrimination analysis
Traffic risk management
ISSN:1687-6180, 1687-6172, 1687-6180
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Popis
Shrnutí:Naive 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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1687-6180
1687-6172
1687-6180
DOI:10.1186/s13634-021-00742-6