Author ORCID Identifier
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.
Mietkiewicz, J., & Madsen, A. (2022). Improvement of the Fine tuning algorithm. University of Antwerp. DOI: 10.21427/7KQM-CQ71
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