Improved Naive Bayes Classification Algorithm for Traffic Risk Management

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 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.

Sentiment Analysis of Nanovest Investment Application Using Naive Bayes Algorithm

“…This study analyzes all tweets containing the "nanovest" keyword, Google Play reviews, and App Store reviews, using the Naïve Bayes method to classify the sentiment. The Naive Bayesian classification algorithm is widely employed in the analysis of big data and diverse fields owing to its simple yet efficient algorithmic structure [7]. In addition to the Naïve Bayes method, a text mining process has been conducted to extract and process information, patterns, and knowledge that were previously unknown [8].…”