Naive Bayes: Applications, Variations and Vulnerabilities- A Review of Literature with Code Snippets for Implementation
By Indika Wickramasinghe and Harsha Kalutarage
The purpose of code snippets provided in this repository is to demonstrate to the reader how a Naive Bayes (NB) classifier can be trained for different applications scenarios (selected) and its vulnerabilities, as discussed in our paper [1]. Note that building and deploying NB-enabled systems in industry settings would be more complicated than this simplified examples (toy examples) and such a discussion is out of the scope of this work.
Citation request: If you found this post and/or the R code snippets are useful, please be kind enough to cite below article in your work:
[1] Wickramasinghe, I., & Kalutarage, H. (2020). Naive Bayes: Applications, Variations and Vulnerabilities- A Review of Literature with Code Snippets for Implementation. Soft Computing https://doi.org/10.1007/s00500-020-05297-6