Learning customized and optimized lists of rules with mathematical programming

Published

Journal Article

© 2018, Springer-Verlag GmbH Germany, part of Springer Nature and The Mathematical Programming Society. 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.

Full Text

Duke Authors

Cited Authors

  • Rudin, C; Ertekin, Ş

Published Date

  • December 1, 2018

Published In

Volume / Issue

  • 10 / 4

Start / End Page

  • 659 - 702

Electronic International Standard Serial Number (EISSN)

  • 1867-2957

International Standard Serial Number (ISSN)

  • 1867-2949

Digital Object Identifier (DOI)

  • 10.1007/s12532-018-0143-8

Citation Source

  • Scopus