Mathematical Programming Computation, Volume 10, Issue 4, December 2018

Font Size:  Small  Medium  Large

Learning customized and optimized lists of rules with mathematical programming

Cynthia Rudin, ?eyda Ertekin

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.


Full Text: PDF

mpc footer
© MPS 2008-2018