Learning customized and optimized lists of rules with mathematical programming

Date

2018-12-01

Author

Rudin, Cynthia
Ertekin Bolelli, Şeyda

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Theoretical Computer Science, Software

URI

https://hdl.handle.net/11511/35666

Journal

MATHEMATICAL PROGRAMMING COMPUTATION

DOI

https://doi.org/10.1007/s12532-018-0143-8

Collections

Department of Computer Engineering, Article

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Citation Formats

C. Rudin and Ş. Ertekin Bolelli, “Learning customized and optimized lists of rules with mathematical programming,” MATHEMATICAL PROGRAMMING COMPUTATION, pp. 659–702, 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/35666.