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

Wickramasinghe, Indika; Kalutarage, Harsha

Authors

Indika Wickramasinghe

Harsha Kalutarage



Abstract

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.

Citation

WICKRAMASINGHE, I. and KALUTARAGE, H. 2021. Naive Bayes: applications, variations and vulnerabilities: a review of literature with code snippets for implementation. Soft computing [online], 25(3), pages 2277-2293. Available from: https://doi.org/10.1007/s00500-020-05297-6

Journal Article Type Article
Acceptance Date Sep 9, 2020
Online Publication Date Sep 9, 2020
Publication Date Feb 28, 2021
Deposit Date Sep 21, 2020
Publicly Available Date Sep 10, 2021
Journal Soft Computing
Print ISSN 1432-7643
Electronic ISSN 1433-7479
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 25
Issue 3
Pages 2277-2293
DOI https://doi.org/10.1007/s00500-020-05297-6
Keywords Naïve Bayes; Probabilistic classification; Machine learning vulnerabilities; R code snippets
Public URL https://rgu-repository.worktribe.com/output/968439