A naive learning algorithm for class‐bridge‐decomposable multidimensional Bayesian network classifiers

Multidimensional Bayesian network classifier (MBC) has become a popular classification model because of their intuitive graphical representation ability among class variables. But learning MBC and performing multidimensional classification based on the MBC can be very computationally demanding. For the tractability of performing multidimensional classification, a class‐bridge‐decomposable (CB‐decomposable) MBC model is proposed and it alleviates the computation complexity. But there are few works to efficiently and systematically learn the CB‐decomposable MBC model. Thus, we focus on addressing a naive learning algorithm of CB‐decomposable MBCs. Briefly, we learn the CB‐decomposable MBC model by dividing it into three components: class subgraph, bridge subgraph, and feature subgraph. First, we analyze why the class subgraph can be learned based on general Bayesian network learning methods. Second, we give how to learn bridge subgraph based on information gain ratio. Third, to make the CB‐decomposable MBC model effective and simple, we also study the learning and updating strategies of feature subgraph. Further, we propose the naive learning algorithm of the CB‐decomposable MBC. Finally, by comparing with other methods on several benchmark datasets, experimental results illustrate that our naive learning algorithm not only has higher accuracies, lower learning, and classification times but also has simple and intuitive representation ability.