Learning customized and optimized lists of rules with mathematical programming

We introduce a mathematical programming approach to building rule lists, which are a type of interpretable, nonlinear, and logical machine learning classifier involving IF-THEN rules. Unlike traditional decision tree algorithms like CART and C5.0, this method does not use greedy splitting and prunin...

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Published in:	Mathematical programming computation Vol. 10; no. 4; pp. 659 - 702
Main Authors:	Rudin, Cynthia, Ertekin, Şeyda
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2018 Springer Nature B.V
Subjects:	Accuracy Decision trees Digital Object Identifier Full Length Paper Machine learning Mathematical analysis Mathematical models Mathematical programming Mathematics Mathematics and Statistics Mathematics of Computing Operations Research/Decision Theory Optimization Pruning Sparsity Theory of Computation 68T05 Learning and adaptive systems Learning and adaptive systems Sparsity 90C11 Mixed integer programming Mixed-integer programming 62-04 Explicit machine computation and programs (not the theory of computation or programming) Decision lists Interpretable modeling Decision trees Artificial intelligence Associative classification 68T05—Computer Science
ISSN:	1867-2949, 1867-2957
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Abstract	We introduce a mathematical programming approach to building rule lists, which are a type of interpretable, nonlinear, and logical machine learning classifier involving IF-THEN rules. Unlike traditional decision tree algorithms like CART and C5.0, this method does not use greedy splitting and pruning. Instead, it aims to fully optimize a combination of accuracy and sparsity, obeying user-defined constraints. This method is useful for producing non-black-box predictive models, and has the benefit of a clear user-defined tradeoff between training accuracy and sparsity. The flexible framework of mathematical programming allows users to create customized models with a provable guarantee of optimality. The software reviewed as part of this submission was given the DOI (Digital Object Identifier) https://doi.org/10.5281/zenodo.1344142 .
AbstractList	We introduce a mathematical programming approach to building rule lists, which are a type of interpretable, nonlinear, and logical machine learning classifier involving IF-THEN rules. Unlike traditional decision tree algorithms like CART and C5.0, this method does not use greedy splitting and pruning. Instead, it aims to fully optimize a combination of accuracy and sparsity, obeying user-defined constraints. This method is useful for producing non-black-box predictive models, and has the benefit of a clear user-defined tradeoff between training accuracy and sparsity. The flexible framework of mathematical programming allows users to create customized models with a provable guarantee of optimality. The software reviewed as part of this submission was given the DOI (Digital Object Identifier) https://doi.org/10.5281/zenodo.1344142 . We introduce a mathematical programming approach to building rule lists, which are a type of interpretable, nonlinear, and logical machine learning classifier involving IF-THEN rules. Unlike traditional decision tree algorithms like CART and C5.0, this method does not use greedy splitting and pruning. Instead, it aims to fully optimize a combination of accuracy and sparsity, obeying user-defined constraints. This method is useful for producing non-black-box predictive models, and has the benefit of a clear user-defined tradeoff between training accuracy and sparsity. The flexible framework of mathematical programming allows users to create customized models with a provable guarantee of optimality. The software reviewed as part of this submission was given the DOI (Digital Object Identifier) https://doi.org/10.5281/zenodo.1344142.
Author	Rudin, Cynthia Ertekin, Şeyda
Author_xml	– sequence: 1 givenname: Cynthia surname: Rudin fullname: Rudin, Cynthia email: cynthia@cs.duke.edu organization: Departments of Computer Science, Electrical and Computer Engineering, and Statistical Science, Duke University – sequence: 2 givenname: Şeyda surname: Ertekin fullname: Ertekin, Şeyda organization: Department of Computer Engineering, Middle Eastern Technical University, MIT Sloan School of Management, Massachusetts Institute of Technology
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Keywords	68T05 Learning and adaptive systems Learning and adaptive systems Sparsity 90C11 Mixed integer programming Mixed-integer programming 62-04 Explicit machine computation and programs (not the theory of computation or programming) Decision lists Interpretable modeling Decision trees Artificial intelligence Associative classification 68T05—Computer Science
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Snippet	We introduce a mathematical programming approach to building rule lists, which are a type of interpretable, nonlinear, and logical machine learning classifier...
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SubjectTerms	Accuracy Decision trees Digital Object Identifier Full Length Paper Machine learning Mathematical analysis Mathematical models Mathematical programming Mathematics Mathematics and Statistics Mathematics of Computing Operations Research/Decision Theory Optimization Pruning Sparsity Theory of Computation
Title	Learning customized and optimized lists of rules with mathematical programming
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