Optimizing Sparse Boolean Rule Networks using Mixed Integer Linear Programming

Thomas Pammer, Johannes Fürnkranz, Markus Sinnl

We study the problem of learning accurate and cognitively simple binary classifiers that are directly interpretable as Boolean rules. We propose mixed integer linear programming (M…