# Randomized Feature and Bootstrapped Naive Bayes Classification
## 5 Results
### Overall Comparative Performance
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & **Top Accuracy (Mean)** & **Top Accuracy (Mean)** & **Friedman _p-Value_** &
\begin{tabular}{c} **Significant Wilcoxon Post Hoc** \\ **(vs. RFB-NB)** \\ \end{tabular} \\ \hline Breast Cancer Wisconsin & RF & RF & 0.0319 & RF better (_p_ = 0.0494) \\  & (0.958) & (0.957) & & \\
**Pima Indians Diabetes** & RFB-NB & NB & 0.0357 & RFB-NB better than KNN (_p_ = 0.0199) \\
**Heart Disease** & RFB-NB (0.848) & RFB-NB (0.847) & \textless{}0.0001 & RFB-NB better than KNN \\ Indian Liver Patients & RF & RF & \textless{}0.0001 & RF (_p_ = 0.0020), KNN (_p_ = 0.0078) \\  & (0.701) & (0.677) & \textless{}0.0001 & better than RFB-NB \\  & RF & RF & & \\ Hepatitis C Patients & (0.947) & (0.938) & 0.2842 & None \\
**Heart Failure Clinical** & NB & NB & 0.0005 & RFB-NB better than KNN (_p_ = 0.0076) \\
**Records** & RF & RF & RF better (_p_ = 0.0039), \\
**Bank Marketing** & (0.896) & (0.872) & \textless{}0.0001 & RFB-NB better than NB (_p_ = 0.0098) \\
**German Credit** & RF & RF & 0.0001 & RFB-NB better than KNN (_p_ = 0.0020) \\  & (0.751) & (0.732) & 0.0001 & RFB-NB better than KNN (_p_ = 0.0020) \\  & NB & NB & 0.1644 & None \\ Telecom Chum Prediction & (0.550) & (0.538) & 0.1644 & None \\  & RF & RF & 0.0001 & RFB-NB better than KNN (_p_ = 0.0076) \\
**Bike Sharing** & (0.906) & (0.906) & 0.0003 & RFB better (_p_ = 0.0391), \\
**Ionosphere** & RF & RF & 0.0004 & RFB-NB better than KNN (_p_ = 0.0117) \\ Waveform Database Generator & RF & RF & RF better (_p_ = 0.0020) \\  & (0.841) & (0.840) & 0.0002 & RF better (_p_ = 0.0020) \\ Room Occupancy & KNN & KNN & \textless{}0.0001 & RF (_p_ = 0.0420), KNN (_p_ = 0.0020) \\  & (0.997) & (0.997) & \textless{}0.0001 & better than RFB-NB \\ Zoo & RFB-NB (0.960) & RFB-NB (0.961) & 0.1518 & None \\
**QSAR Bioconcentration** & RF & RF & \textless{}0.0003 & RFB-NB better than NB (_p_ = 0.0020) \\
**Classes** & (0.680) & (0.665) & 0.0003 & and RF-NB (_p_ = 0.0176) \\
**Secondary Mushroom** & RFB-NB (0.583) & NB & 0.0114 & RFB-NB better than RF (_p_ = 0.0117) \\
**Rice** & RF & RF & \textless{}0.0001 & RFB-NB better than KNN (_p_ = 0.0020) \\  & (0.924) & (0.924) & (0.924) & \\ Seeds & RF & RF & 0.1749 & None \\  & (0.909) & (0.909) & & \\
**White Wine Quality** & RF & RF & \textless{}0.0001 & (_p_ = 0.0020, 0.0215), \\  & (0.541) & (0.491) & & RF better (_p_ = 0.0020) \\  & KNN & KNN & \textless{}0.0001 & KNN, RF, and NB better than \\  & (0.915) & (0.905) & \textless{}0.0001 & RFB-NB (_p_ = 0.0020, 0.0059, 0.0059) \\ \hline \hline \end{tabular} Note.: Datasets indicated in bold denote cases where the RFB-NB classifier significantly outperformed at least one competing classifier (Wilcoxon signed-rank test, \(p\) \(<\) 0.05).  
\end{table} 
Table 2: Dataset-level comparative performance with Friedman test statistics and significant Wilcoxon signed-rank comparisons across 20 datasets. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Mean** & **Mean** & **Average** \\  & **Accuracy** & **Std. Dev.** & **Rank** \\ \hline RF & 0.8061 & 0.0591 & 1.875 \\ RFB-NB & 0.7869 & 0.0447 & 2.375 \\ RF-NB & 0.7798 & 0.0449 & 3.000 \\ NB & 0.7672 & 0.0573 & 3.775 \\ KNN & 0.7494 & 0.0523 & 3.975 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Aggregate comparative results of classification accuracy across 20 datasets.