Bayes Point Rule Set Learning

This paper proposes an effective bottom-up extension of the popular FIND-S algorithm to learn (monotone) DNF-type rulesets. The algorithm greedily finds a partition of the positive examples. The produced monotone DNF is a set of conjunctive rules, each corresponding to the most specific rule consistent with a part of positive and all negative examples. We also propose two principled extensions of this method, approximating the Bayes Optimal Classifier by aggregating monotone DNF decision rules. Finally, we provide a methodology to improve the explainability of the learned rules while retaining their generalization capabilities. An extensive comparison with state-of-the-art symbolic and statistical methods on several benchmark data sets shows that our proposal provides an excellent balance between explainability and accuracy.