Classification Rules in Relaxed Logical Form


ML algorithms that produce rule-based predictions in Conjunctive Normal form (CNF) or in Disjunctive Normal form (DNF) are arguably some of the most interpretable ones. Although CNF/DNF rules are considered interpretable in practice, propositional logic has other very interpretable representations which are more expressive. In this paper, we generalize CNF/DNF rules and introduce relaxed-CNF rules, which is motivated by the popular usage of checklists in the medical domain. We consider relaxed definitions of standard OR/AND operators which allow exceptions in the construction of a clause and also in the selection of clauses in a rule. While the combinatorial structure of relaxed-CNF rules offers exponential succinctness, the naive learning techniques are computationally expensive. The primary contribution of this paper is to propose a novel incremental mini-batch learning procedure, called CRR, that employs advances in the combinatorial solvers and efficiently learns relaxed-CNF rules. Our experimental analysis demonstrates that CRR can generate relaxed-CNF rules which are more accurate compared to CNF rules and sparser compared to decision lists.

In Proceedings of ECAI