Abstract
Khalil El Hindi has developed a fine-tuning algorithm toimprove the classification accuracy of the Naive Bayes. His algorithm optimizes the conditional probability tables of the Naive Bayes after thetraining 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 performanceof 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, tofine-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.
Original language | English |
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Journal | University of Antwerp |
DOIs | |
Publication status | Published - 2022 |
Keywords
- fine-tuning algorithm
- classification accuracy
- Naive Bayes
- conditional probability tables
- training phase
- misclassification
- performance analysis
- Brier score
- benchmark data sets