Gaussian Naïve Bayes Classification Algorithm for Drought and Flood Risk Reduction

2020 ◽  
pp. 49-62
Author(s):  
Oluwatobi Aiyelokun ◽  
Gbenga Ogunsanwo ◽  
Akintunde Ojelabi ◽  
Oluwole Agbede
Keyword(s):  
Risk Reduction ◽  
Flood Risk ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Algorithm ◽  
Drought And Flood ◽  
Naive Bayes Classification ◽  
Flood Risk Reduction ◽  
Naïve Bayes Classification

Naive Bayes Classification Algorithm Based on Optimized Training Data

2012 ◽  
Vol 490-495 ◽  
pp. 460-464 ◽  
Author(s):  
Xiao Dan Zhu ◽  
Jin Song Su ◽  
Qing Feng Wu ◽  
Huai Lin Dong
Keyword(s):  
Large Scale ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Algorithm ◽  
Training Data ◽  
Large Scale Data ◽  
Naive Bayes Classification ◽  
Naïve Bayes Classification ◽  
Effectiveness Threshold ◽  
Selection Of

Naive Bayes classification algorithm is an effective simple classification algorithm. Most researches in traditional Naive Bayes classification focus on the improvement of the classification algorithm, ignoring the selection of training data which has a great effect on the performance of classifier. And so a method is proposed to optimize the selection of training data in this paper. Adopting this method, the noisy instances in training data are eliminated by user-defined effectiveness threshold, improving the performance of classifier. Experimental results on large-scale data show that our approach significantly outperforms the baseline classifier.

scholarly journals Improved naive Bayes classification algorithm for traffic risk management

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Hong Chen ◽  
Songhua Hu ◽  
Rui Hua ◽  
Xiuju Zhao
Keyword(s):  
Numerical Simulation ◽  
Naive Bayes ◽  
Robustness Analysis ◽  
Big Data Analysis ◽  
Naïve Bayes ◽  
Classification Algorithm ◽  
Feature Weighting ◽  
Discrimination Analysis ◽  
Naive Bayes Classification ◽  
Naïve Bayes Classification

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.

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