Learning Optimized Or's of And's

2015/11/06 by Tong Wang, Cynthia Rudin, Wang, Tong +1 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Data Mining Algorithms and Applications #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #Machine Learning and Data Classification

paper · pdf · doi:10.48550/arxiv.1511.02210

openalex publication_date 2015/11/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Or's of And's (OA) models are comprised of a small number of disjunctions of conjunctions, also called disjunctive normal form. An example of an OA model is as follows: If (x₁ = `blue' AND x<sub>2</sub>= `middle') OR (x₁ = `yellow'), then predict Y=1, else predict Y=0. Or's of And's models have the advantage of being interpretable to human experts, since they are a set of conditions that concisely capture the characteristics of a specific subset of data. We present two optimization-based machine learning frameworks for constructing OA models, Optimized OA (OOA) and its faster version, Optimized OA with Approximations (OOAx). We prove theoretical bounds on the properties of patterns in an OA model. We build OA models as a diagnostic screening tool for obstructive sleep apnea, that achieves high accuracy with a substantial gain in interpretability over other methods.

Citations

• The robust beauty of improper linear models in decision making.

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