Naive Bayes: applications, variations and vulnerabilities: a review of literature with code snippets for implementation.
Wickramasinghe, Indika; Kalutarage, Harsha
Dr Harsha Kalutarage email@example.com
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.
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|
|Peer Reviewed||Peer Reviewed|
|Keywords||Naïve Bayes; Probabilistic classification; Machine learning vulnerabilities; R code snippets|
WICKRAMASINGHE 2021 Naive bayes
You might also like
Keep the moving vehicle secure: context-aware intrusion detection system for in-vehicle CAN bus security.
Developing secured android applications by mitigating code vulnerabilities with machine learning.
Robust, effective and resource efficient deep neural network for intrusion detection in IoT networks.
Improving intrusion detection through training data augmentation.
Reasoning with counterfactual explanations for code vulnerability detection and correction.