Naïve Bayes (NB) is a well-known probabilistic classification algorithm. It is a simple but
efficient algorithm with a wide variety of real-world applications, ranging from product …

We have had to wait over 30 years since the naive Bayes model was first introduced in 1960
for the so-called Bayesian network classifiers to resurge. Based on Bayesian networks …

Naïve Bayes is one of the most popular data mining algorithms. Its efficiency comes from the
assumption of attribute independence, although this might be violated in many real-world …

Text classification is the task of assigning predefined categories to natural language
documents, and it can provide conceptual views of document collections. The Naïve Bayes …

Naive Bayes (NB) continues to be one of the top 10 data mining algorithms due to its
simplicity, efficiency and efficacy. Of numerous proposals to improve the accuracy of naive …

Due to its simplicity, efficiency, and efficacy, naive Bayes (NB) has continued to be one of the
top 10 algorithms in the data mining and machine learning community. Of numerous …

Due to its easiness to construct and interpret, along with its good performance, naive Bayes
(NB) is widely used to address classification problems in real-world applications. In order to …

Because learning an optimal Bayesian network classifier is an NP-hard problem, learning-
improved naive Bayes has attracted much attention from researchers. In this paper, we …

Naive Bayes is one of the most widely used algorithms in classification problems because of
its simplicity, effectiveness, and robustness. It is suitable for many learning scenarios, such …

To interpret genetic variants discovered from next‐generation sequencing, integration of
heterogeneous information is vital for success. This article describes a framework named …