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Thomas Pammer

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#machine learning Preprint Oct 2026

Optimizing Sparse Boolean Rule Networks using Mixed Integer Linear Programming

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 (MILP) formulations for training sparse disjunctive normal form (DNF) networks, viewed as small networks with a conjunctive hidden layer and a...

Thomas Pammer, Johannes Fürnkranz, Markus Sinnl · 0 citations

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