Integrating Naïve Bayes and FOIL
Niels Landwehr, Kristian Kersting, Luc De Raedt · Lirias · 2007
A novel relational learning approach that tightly integrates the naive Bayes learning scheme with the inductive logic programming rule-learner FOIL is presented. In contrast to previous combinations that have employed naive Bayes only for post-processing the rule sets, the presented approach employs the naive Bayes criterion to guide its search directly. The proposed technique is implemented in the NFOIL and TFOIL systems, which employ standard naive Bayes and tree augmented naive Bayes models respectively. We show that these integrated approaches to probabilistic model and rule learning outperform post-processing approaches. They also yield significantly more accurate models than simple rule learning and are competitive with more sophisticated ILP systems.