The Naive Bayesian classifier (NBC) is a well-known classification model that has a simple structure, low training complexity, excellent scalability, and good classification performances. However, the NBC has two key limitations: (1) it is built upon the strong assumption that condition attributes are independent, which often does not hold in real-life, and (2) the NBC does not handle continuous attributes well. To overcome these limitations, this paper presents a novel approach for NBC construction, called mixed-…