Author ORCID Identifier

Document Type

Conference Paper



Publication Details

BNAIC 2022 conference

Proceedings online


Khalil El Hindi has developed a fine-tuning algorithm to
improve the classification accuracy of the Naive Bayes. His algorithm optimizes the conditional probability tables of the Naive Bayes after the
training phase. The values of the probabilities of a variable are modified if it causes misclassification of a training instance. The algorithm out-performs in many cases the Naive Bayes. We analyze the performance
of the algorithm, discussed its issues, and compare it to a modified algorithm. The new algorithm simplifies the formula used in the fine-tuning algorithm and uses a more efficient scoring metric, the Brier score, to
fine-tune the probabilities. The new algorithm shows an improvement in terms of classification accuracy on benchmark data sets compared to the Naive Bayes and fine tuned Naive Bayes.



Collaborative intelligence for safety Critical systems (CISC)

Creative Commons License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.