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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Bibliographic Details
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
Online Access:Get full text
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Summary: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 .
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ISSN:1867-2949
1867-2957
DOI:10.1007/s12532-018-0143-8