Improved naive Bayes classification algorithm for traffic risk management

Hong Chen,Rui Hua,Songhua Hu,Xiuju Zhao

doi:10.1186/s13634-021-00742-6

Abstract

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.

Highlights

There are many ways to construct classifiers, such as the Bayesian method, decision tree method, case-based learning method, artificial neural network method, support vector machine method, genetic algorithm method, rough set method, fuzzy set method, and so on
Ten thousand samples are randomly selected from the standard normal distribution N (0,1), and the accuracy of the model is tested by gradually increasing the sample size
All the indexes are far lower than the results of the improved naive Bayesian classification algorithm

Summary

Introduction

There are many ways to construct classifiers, such as the Bayesian method, decision tree method, case-based learning method, artificial neural network method, support vector machine method, genetic algorithm method, rough set method, fuzzy set method, and so on. With the maturity of big data technology and the improvement of database, AI-related methods are more and more used in the field of traffic risk management, including support vector machine [21], RBF neural network [22], deep learning [23], fuzzy rule base [24], etc. With the rapid growth of traffic data and the improvement of its computing power, the machine learning algorithm has become a potentially important means to deal with traffic risk management [26]. The improved naive Bayes classification algorithm is applied to the scene of traffic risk management to effectively predict and classify the driver’s driving risk and implement effective risk management.

Bayes theory

Feature-weighted naive Bayes classification algorithm

Laplace calibration

Findings

Result and discussion