LNBC: A Link-Based Naive Bayes Classifier

Many databases store data in relational format, with different types of entities and information about links between the entities. Link-based classification is the problem of predicting the class label of a target entity given information about features of the entity and about features of the related entities. A natural approach to link-based classification is to upgrade standard classification methods from the propositional, single-table testing. In this paper we propose a new classification rule for upgrading naive Bayes classifiers (NBC). Previous work on relational NBC has achieved the best results with link independency assumption which says that the probability of each link to an object is independent from the other links to the object. We formalize our method by breaking it into two parts: (1) the independent influence assumption: that the influence of one path from the target object to a related entity is independent of another. We consider object-path independency and (2) the independent feature assumption of NBC: that features of the target entity and a related entity are probabilistically independent given a target class label. We derive a new relational NBC rule that places more weight on the target entity features than formulations of the link independency assumption. The new NBC rule yields higher accuracies on three benchmark datasets-Mutagenesis, MovieLens, and Cora-with average improvements ranging from 2% to 10%.