Improvement of the Fine tuning algorithm

Authors

Joseph Mietkiewicz, Technological University DublinFollow
Anders Madsen, Aalborg University

Author ORCID Identifier

https://orcid.org/0009-0007-3109-5865

Document Type

Conference Paper

Disciplines

1.1 MATHEMATICS

Publication Details

BNAIC 2022 conference

Proceedings online

https://bnaic2022.uantwerpen.be/accepted-submissions/

Abstract

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.

DOI

https://doi.org/10.21427/7KQM-CQ71

Recommended Citation

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

Funder

Collaborative intelligence for safety Critical systems (CISC)

Creative Commons License

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