Randomized Feature and Bootstrapped Naive Bayes Classification
Abstract
1. Introduction
2. Related Theory
2.1. Gaussian Naive Bayes
2.2. Decision Boundary
2.3. Parameter Estimation and Implementation
- Prior Probability: The proportion of training samples in class is used:
- Conditional Probabilities For continuous features, the Gaussian likelihood is parameterized using the estimated means and variances .
3. Proposed Methodology
3.1. Data Partitioning and Randomized Feature Selection
3.2. Bootstrap Sample Generation for Training
3.3. Gaussian Naive Bayes Training
3.4. Classification and Prediction Aggregation
3.5. Variance Reduction in RFB-NB
3.6. Cross-Validation and Model Assessment
3.7. Comparison of RFB-NB with TAN, WNB, and RF
4. Datasets
4.1. Dataset Description
4.2. Dataset Characteristics and Selection Rationale
- (1)
- Healthcare and Medical DiagnosticsThese 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 AnalyticsDatasets 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 ClassificationDatasets involving an analysis of signals or sensor measurements for classification tasks.
- (4)
- Biological and Environmental ClassificationDatasets focused on classifying biological or agricultural samples and assessing environmental safety.
- Zoo [44]: Multi-class classification of animals into predefined categories.
- QSAR Bioconcentration Classes [45]: Classification of chemical compounds based on manually curated bioconcentration factors (BCF, fish) for QSAR modeling.
- Secondary Mushroom [46]: Binary classification (edible vs. poisonous mushrooms).
- Rice [47]: Binary classification identifying rice varieties (Cammeo vs. Osmancik).
- Seeds [48]: Multi-class classification of wheat kernel varieties based on geometrical and morphological features obtained using X-ray imaging techniques.
- (5)
- Product Quality AssessmentDatasets evaluating the quality or condition of consumer products.
4.3. Baseline Classification Methods
- (1)
- Random Forest
- (2)
- K-Nearest Neighbors
5. Results
5.1. Comparative Analysis by Domain
5.1.1. Healthcare and Medical Diagnostics
5.1.2. Financial and Business Analytics
5.1.3. Signal Processing and Sensor-Based Classification
5.1.4. Biological and Environmental Classification
5.1.5. Product Quality Assessment
5.2. Stability and Robustness Evaluation
5.3. Overall Comparative Performance
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Reference | Dataset | n | p | Response | Distribution Percentage (%) |
---|---|---|---|---|---|
[31] | Breast Cancer Wisconsin | 569 | 30 | Malignant/benign | 37.2/62.8 |
[32] | Pima Indians Diabetes | 768 | 8 | Diabetic/non-diabetic | 34.9/65.1 |
[33] | Heart Disease | 270 | 13 | Presence/absence of disease | 44.4/55.6 |
[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 C/fibrosis/cirrhosis | 91.4/3.2/2.5/2.1/0.8 |
[36] | Heart Failure Clinical Records | 299 | 12 | Death/not death | 32.1/67.9 |
[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 radar return | 64.1/35.9 |
[42] | Waveform Database Generator | 5000 | 21 | Class 1/2/3 | 33.2/32.9/33.9 |
[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/ 8.0/9.9 |
[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 | Cammeo/Osmancik | 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 |
Dataset | Top Accuracy (Mean) | Top Accuracy (Mean) | Friedman p-Value | Significant Wilcoxon Post Hoc (vs. RFB-NB) |
---|---|---|---|---|
Breast Cancer Wisconsin | RF (0.958) | RF (0.957) | 0.0319 | RF better (p = 0.0494) |
Pima Indians Diabetes | RFB-NB (0.753) | NB (0.748) | 0.0357 | RFB-NB better than KNN (p = 0.0199) |
Heart Disease | RFB-NB (0.848) | RFB-NB (0.847) | <0.0001 | RFB-NB better than KNN |
Indian Liver Patients | RF (0.701) | RF (0.677) | <0.0001 | RF (p = 0.0020), KNN (p = 0.0078) better than RFB-NB |
Hepatitis C Patients | RF (0.947) | RF (0.938) | 0.2842 | None |
Heart Failure Clinical Records | NB (0.796) | NB (0.784) | 0.0005 | RFB-NB better than KNN (p = 0.0076) |
Bank Marketing | RF (0.896) | RF (0.872) | <0.0001 | RF better (p = 0.0039), RFB-NB better than NB (p = 0.0098) |
German Credit | RF (0.751) | RF (0.732) | 0.0001 | RFB-NB better than KNN (p = 0.0020) |
Telecom Churn Prediction | NB (0.550) | NB (0.538) | 0.1644 | None |
Bike Sharing | RF (0.906) | RF (0.906) | 0.0001 | RFB-NB better than KNN (p = 0.0076) |
Ionosphere | RF (0.929) | RF (0.928) | 0.0004 | RF better (p = 0.0391), RFB-NB better than KNN (p = 0.0117) |
Waveform Database Generator | RF (0.841) | RF (0.840) | 0.0002 | RF better (p = 0.0020) |
Room Occupancy | KNN (0.997) | KNN (0.997) | <0.0001 | RF (p = 0.0420), KNN (p = 0.0020) better than RFB-NB |
Zoo | RFB-NB (0.960) | RFB-NB (0.961) | 0.1518 | None |
QSAR Bioconcentration Classes | RF (0.680) | RF (0.665) | 0.0003 | RFB-NB better than NB (p = 0.0020) and RF-NB (p = 0.0176) |
Secondary Mushroom | RFB-NB (0.583) | NB (0.628) | 0.0114 | RFB-NB better than RF (p = 0.0117) |
Rice | RF (0.924) | RF (0.924) | <0.0001 | RFB-NB better than KNN (p = 0.0020) |
Seeds | RF (0.909) | RF (0.909) | 0.1749 | None |
White Wine Quality | RF (0.541) | RF (0.491) | <0.0001 | RFB-NB better than NB and RF-NB (p = 0.0020, 0.0215), RF better (p = 0.0020) |
Car Evaluation | KNN (0.915) | KNN (0.905) | <0.0001 | KNN, RF, and NB better than RFB-NB (p = 0.0020, 0.0059, 0.0059) |
Model | Mean Accuracy | Mean Std. Dev. | Average Rank |
---|---|---|---|
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 |
Model | Mean Accuracy | Mean Std. Dev. | Average Rank |
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 |
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Phatcharathada, B.; Srisuradetchai, P. Randomized Feature and Bootstrapped Naive Bayes Classification. Appl. Syst. Innov. 2025, 8, 94. https://doi.org/10.3390/asi8040094
Phatcharathada B, Srisuradetchai P. Randomized Feature and Bootstrapped Naive Bayes Classification. Applied System Innovation. 2025; 8(4):94. https://doi.org/10.3390/asi8040094
Chicago/Turabian StylePhatcharathada, Bharameeporn, and Patchanok Srisuradetchai. 2025. "Randomized Feature and Bootstrapped Naive Bayes Classification" Applied System Innovation 8, no. 4: 94. https://doi.org/10.3390/asi8040094
APA StylePhatcharathada, B., & Srisuradetchai, P. (2025). Randomized Feature and Bootstrapped Naive Bayes Classification. Applied System Innovation, 8(4), 94. https://doi.org/10.3390/asi8040094