Naive Bayes: applications, variations and vulnerabilities: a review of literature with code snippets for implementation

By Indika P. Wickramasinghe, Harsha Kalutarage. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 2020

322 citations recorded by the source corpus.

Naïve Bayes (NB) is a well-known probabilistic classification algorithm. It is a simple but efficient algorithm with a wide variety of real-world applications, ranging from product recommendations through medical diagnosis to controlling autonomous vehicles. Due to the failure of real data satisfying the assumptions of NB, there are available variations of NB to cater general data. With the unique applications for each variation of NB, they reach different levels of accuracy. This manuscript surveys the latest applications of NB and discusses its variations in different settings. Furthermore, recommendations are made regarding the applicability of NB while exploring the robustness of the algorithm. Finally, an attempt is given to discuss the pros and cons of NB algorithm and some vulnerabilities, with related computing code for implementation.

Subfields: Artificial Intelligence, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/bddda5b2cba5f80793df692c72c66121ec35eba2