Conjecturing-Based Discovery of Patterns in Data

J. Paul Brooks (), David J. Edwards (), Craig E. Larson () and Nico Van Cleemput ()
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J. Paul Brooks: Department of Information Systems, Virginia Commonwealth University, Richmond, Virginia 23284
David J. Edwards: Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, Virginia 23284
Craig E. Larson: Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, Virginia 23284
Nico Van Cleemput: Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium

INFORMS Joural on Data Science, 2024, vol. 3, issue 2, 179-202

Abstract: We propose the use of a conjecturing machine that suggests feature relationships in the form of bounds involving nonlinear terms for numerical features and Boolean expressions for categorical features. The proposed C onjecturing framework recovers known nonlinear and Boolean relationships among features from data. In both settings, true underlying relationships are revealed. We then compare the method to a previously proposed framework for symbolic regression on the ability to recover equations that are satisfied among features in a data set. The framework is then applied to patient-level data regarding COVID-19 outcomes to suggest possible risk factors that are confirmed in the medical literature. Discovering patterns in data is a first step toward establishing causal relationships, which can be the basis for effective decision making.

Keywords: automated conjecturing; computational scientific discovery; interpretable artificial intelligence; nonlinear pattern discovery; Boolean pattern discovery (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijds:v:3:y:2024:i:2:p:179-202

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