Multi-relational Structural Bayesian Classifier

In the traditional na¨ıve Bayes classification method, training data are represented as a single table (or database relation), where each row corresponds to an example and each column to a predictor variable or a target variable. In this paper we propose a multi-relational extension of the na¨ıve Bayes classification method that is characterized by three aspects: first, an integrated approach in the computation of the posterior probabilities for each class; second, the applicability to both discrete and continuous attributes; third, the consideration of knowledge on the data model embedded in the database schema during the generation of classification rules. The proposed method has been implemented in the new system Mr-SBC and tested on three benchmark tasks. Results on predictive accuracy favour our system for the most complex task. Mr-SBC also proved to be an efficient multi-relational data mining system with a tight dose integration to a relational DBMS.