# Randomized Feature and Bootstrapped Naive Bayes Classification
###### Abstract  
Naive Bayes (NB) classifiers are widely used for their simplicity, computational efficiency, and interpretability. However, their predictive performance can degrade significantly in real-world settings where the conditional independence assumption is often violated. More complex NB variants address this issue but typically introduce structural complexity or require explicit dependency modeling, limiting their scalability and transparency. This study proposes two lightweight ensemble-based extensions--randomized feature naive Bayes (RF-NB) and randomized feature bootstrapped naive Bayes (RFB-NB)--designed to enhance robustness and predictive stability without altering the underlying NB model. By integrating randomized feature selection and bootstrap resampling, these methods implicitly reduce feature dependence and noise-induced variance. Evaluation across twenty real-world datasets spanning medical, financial, and industrial domains demonstrates that RFB-NB consistently outperformed classical NB, RF-NB, and k-nearest neighbor in several cases. Although random forest achieved higher average accuracy overall, RFB-NB demonstrated comparable accuracy with notably lower variance and improved predictive stability specifically in datasets characterized by high noise levels, large dimensionality, or significant class imbalance. These findings underscore the practical and complementary advantages of RFB-NB in challenging classification scenarios. a 2025 8, 94 202 purpose. [PERSON] and [PERSON] [8] illustrated how decision trees effectively guide feature selection in NB classifiers, paving the way for subsequent advancements. Similarly, [PERSON] and [PERSON] [10] emphasized wrapper methods' ability to directly assess subsets of features based on their predictive capabilities, establishing this as a robust selection strategy. [PERSON] [12] demonstrated the efficacy of correlation-based selection in filtering redundant features from high-dimensional data. Extending these contributions, [PERSON] et al. [13] successfully applied recursive feature elimination to NB in crime prediction contexts. [PERSON] et al. [14] further compared NB and random forests, highlighting the relative strengths and limitations of these classifiers in practical news popularity prediction tasks. Additionally, [PERSON] et al. [15] integrated NB within random forests to enrich ensemble diversity and enhance predictive accuracy across heterogeneous datasets. In parallel, bootstrap sampling methods have proven valuable for enhancing model robustness. Initially introduced by [PERSON] and [PERSON] [16], bootstrap sampling offers a rigorous mechanism to estimate statistical accuracy and model variability. Integration into machine learning has inspired innovations such as bagging and ensemble learning approaches [17; 18]. A notable example is the random forest (RF), proposed by [PERSON] [17], which merges bootstrap sampling with randomized feature selection to yield highly robust classifiers. [PERSON] and [PERSON] [19] illustrated RF's practical efficacy, solidifying its role as a benchmark classifier. Similarly, ensemble techniques leveraging bootstrap methods, such as weighted and tree-augmented NB classifiers, have demonstrated improvements in predictive accuracy and stability [20]. Additionally, randomized kernel k-nearest neighbor (KNN) regression exemplifies the versatility of bootstrap-enhanced ensemble methods [21]. Furthermore, bootstrap methods facilitate the construction of confidence intervals for complex statistical models, including zero-truncated Poisson-Ishita distributions [22]. Another critical enhancement targets NB's simplifying assumption of conditional feature independence. Prior studies [4; 5] investigated scenarios wherein this assumption significantly undermines predictive accuracy. To alleviate such challenges, [PERSON] et al. [23] introduced hidden NB, employing latent variables to model dependencies among features more effectively.