# Randomized Feature and Bootstrapped Naive Bayes Classification
## 6 Conclusions  
This study introduced two extensions of the classical NB classifier--RF-NB and RFB-NB--designed explicitly to address limitations inherent to NB through randomized feature selection and bootstrap aggregation. Extensive empirical evaluations across twenty diverse real-world datasets in medical diagnostics, financial analytics, sensor-based monitoring, biological classification, and product quality assessment illustrated the effectiveness of these methods. While RFB-NB consistently outperformed classical NB, RF-NB, and KNN in several scenarios, RF exhibited slightly higher average accuracy overall. Nevertheless, RFB-NB demonstrated distinct advantages by providing comparable accuracy alongside significantly reduced variance and improved stability. These benefits were particularly pronounced in challenging scenarios, notably datasets affected by considerable noise, high-dimensional feature spaces, or pronounced class imbalance (e.g., Breast Cancer and Telecom Churn datasets).  
Statistical significance analyses using Friedman and Wilcoxon post-hoc tests further confirmed that RFB-NB significantly surpassed at least one benchmark method in 11 of the 20 datasets evaluated. These results emphasize that, despite RF's superior accuracy in some scenarios, RFB-NB offers valuable complementary strengths--robustness and stability--making it an ideal choice for applications where interpretability, reliability, and computational efficiency are essential.  
Future research should focus on exploring adaptive parameter tuning strategies, assessing RFB-NB's performance in regression, multi-label classification, or further imbalanced scenarios, and conducting deeper theoretical investigations to precisely determine the optimal conditions under which RFB-NB delivers its greatest advantages.  
Conceptualization, P.S. and B.P.; methodology, P.S.; software, P.S. and B.P.; validation, P.S. and B.P.; formal analysis, P.S. and B.P.; investigation, P.S.; resources, P.S. and B.P.; data curation, B.P.; writing--original draft preparation, P.S. and B.P.; writing--review and editing, P.S.; visualization, P.S. and B.P.; supervision, P.S.; project administration, P.S.; funding acquisition, P.S. and B.P. All authors have read and agreed to the published version of the manuscript.  
This project is funded by Scholarship for Talent Student to Study Graduate Program in Faculty of Science and Technology Thammasat University, Contact No. TB 1/2023.  
All datasets utilized in this study are publicly available through the UCI Machine Learning Repository ([[https://archive.ics.uci.edu/](https://archive.ics.uci.edu/)]([https://archive.ics.uci.edu/](https://archive.ics.uci.edu/)), accessed on 15 January 2025) and Kaggle ([[https://www.kaggle.com/](https://www.kaggle.com/)]([https://www.kaggle.com/](https://www.kaggle.com/)), accessed on 15 January 2025). Specific references and DOIs for each dataset are provided in Table 1 of the manuscript.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Mean** & **Mean** & **Average** \\  & **Accuracy** & **Std. Dev.** & **Rank** \\ \hline RF & 0.7979 & 0.0619 & 2.025 \\ RFB-NB & 0.7841 & 0.0471 & 2.600 \\ NB & 0.7758 & 0.0575 & 2.925 \\ RF-NB & 0.7726 & 0.0475 & 3.100 \\ KNN & 0.7579 & 0.0551 & 4.350 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Aggregate comparative results of classification weighted-average F1-score across 20 datasets.