Integrating Naïve Bayes and FOIL
Niels Landwehr, Kristian Kersting, Luc De Raedt; 8(18):481−507, 2007.
Abstract
A novel relational learning approach that tightly integrates the naïve Bayes learning scheme with the inductive logic programming rule-learner FOIL is presented. In contrast to previous combinations that have employed naïve Bayes only for post-processing the rule sets, the presented approach employs the naïve Bayes criterion to guide its search directly. The proposed technique is implemented in the NFOIL and TFOIL systems, which employ standard naïve Bayes and tree augmented naïve Bayes models respectively. We show that these integrated approaches to probabilistic model and rule learning outp erform post-processing approaches. They also yield significantly more accurate models than si mple rule learning and are competitive with more sophisticated ILP systems.
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