# Randomized Feature and Bootstrapped Naive Bayes Classification
## 4 Datasets
### Dataset Characteristics and Selection Rationale  
The datasets were carefully selected to rigorously evaluate the proposed methodology across diverse real-world classification tasks. The primary criteria for selection included (i) variability in feature dimensionality, (ii) diversity of response variable types, and (iii) representation of distinct practical domains. The chosen datasets were categorized into the following comprehensive groups, each encompassing multiple datasets:  
1. Healthcare and Medical Diagnostics These datasets involve predicting health-related outcomes using clinical and physiological attributes. * Breast Cancer Wisconsin [31]: Binary classification (malignant vs. benign tumors). * Pima Indians Diabetes [32]: Binary classification (presence or absence of diabetes). * Heart Disease [33]: Binary classification predicting the presence or absence of heart disease based on clinical parameters. * Indian Liver Patients [34]: Binary classification (liver disease prediction). * Hepatitis C Patients [35]: Classification of blood donors and Hepatitis C patients based on laboratory and demographic values. * Heart Failure Clinical Records [36]: Binary classification (survival vs. death outcomes). 2. Financial and Business Analytics Datasets addressing customer behaviors, financial risks, and decision-making processes. * Bank Marketing [37]: Predictive modeling of customer subscription behaviors. * German Credit [38]: Binary classification of creditworthiness (good or bad credit). * Telecom Churn Prediction [39]: Binary classification predicting customer churn behavior. * Bike Sharing [40]: Ordinal classification predicting bike rental demand (categorized as low-, medium-, and high-demand days). 3. Signal Processing and Sensor-Based Classification Datasets involving an analysis of signals or sensor measurements for classification tasks. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Dataset** & **n** & **p** & **Response** & **Distribution** \\  & & & & & & **Percentage (\%)** \\ \hline
[34] & Indian Liver Patients & 583 & 10 & Liver disease/no disease & 28.5/71.5 \\
[35] & Hepatitis C Patients & 612 & 12 & Blood donor/suspect blood donor/hepatitis & 91.4/3.2/2.5/2.1/0.8 \\  & & & & C/fibrosis/cirrhosis & \\
[36] & Heart Failure Clinical & 299 & 12 & Death/not death & 32.1/67.9 \\  & Records & 245,211 & 16 & Subscribed/not Subscribed & 11.5/88.5 \\
[37] & Bank Marketing & 45,211 & 16 & Subscribed/not Subscribed & 11.5/88.5 \\
[38] & German Credit & 1000 & 20 & Good/bad credit & 70/30 \\
[39] & Telecom Churn Prediction & 7043 & 21 & Churn/no churn & 53.7/46.3 \\
[40] & Bike Sharing & 17,389 & 13 & Low-/medium-/high-demand bike days & 34.5/35.6/29.9 \\
[41] & Ionosphere & 351 & 34 & Good/bad return & 64.1/35.9 \\
[42] & Waveform Database Generator & 5000 & 21 & Class 1/2/3 & 33.2/32/39.3/9.3 \\
[43] & Room Occupancy & 10,129 & 18 & Number of occupants (0 to 3) & 81.2/4.5/7.4/6.9 \\
[44] & Zoo & 101 & 17 & Animal class (7 categories) & 40.5/19.8/5.0/12.8/4.0/ \\
[45] & QSAR Bioconcentration Classes & 779 & 9 & Bioaccumulation classes (1 to 3) & 59.1/8.1/32.8 \\
[46] & Secondary Mushroom & 61,068 & 20 & Edible/poisonous & 44.5/55.5 \\
[47] & Rice & 3810 & 7 & Carmneo/Osmanick & 42.8/57.2 \\
[48] & Seeds & 210 & 7 & Three different varieties of wheat: Kama, Rosa, and Canadian & 33.7/32.7/33.6 \\
[49] & White Wine Quality & 4898 & 11 & Score quality (3 to 9) & 0.4/3.3/29.8/44.9/17.9/ \\  & & & & 3.6/0.1 \\
[50] & Car Evaluation & 1728 & 6 & Evaluation level (unacceptable, acceptable, good, very good) & 22.2/4.0/70.0/3.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)Ionosphere [41]: Radar signal classification (good vs. bad signals). * Waveform Database Generator [42]: Multi-class waveform pattern classification. * Room Occupancy [43]: Prediction of room occupancy based on environmental sensors. * Biological and Environmental Classification Datasets focused on classifying biological or agricultural samples and assessing environmental safety.