Naive Bayes

Naive Bayes is a probabilistic classifier renowned for its simplicity and efficiency, primarily aiming to predict class membership based on the conditional independence of features. Current research focuses on enhancing its performance by addressing limitations like the strong independence assumption through techniques such as weighted variable selection, optimal feature projection, and generalized model structures, often incorporating neural networks or ensemble methods. These advancements improve Naive Bayes' accuracy and robustness across diverse applications, including text classification, image analysis, and software defect prediction, making it a valuable tool in various fields.