Learning certifiably optimal rule lists

Conference Paper

We present the design and implementation of a custom discrete optimization technique for building rule lists over a categorical feature space. Our algorithm provides the optimal solution, with a certificate of optimality. By leveraging algorithmic bounds, efficient data structures, and computational reuse, we achieve several orders of magnitude speedup in time and a massive reduction of memory consumption. We demonstrate that our approach produces optimal rule lists on practical problems in seconds. This framework is a novel alternative to CART and other decision tree methods.

Full Text

Duke Authors

Cited Authors

  • Angelino, E; Larus-Stone, N; Alabi, D; Seltzer, M; Rudin, C

Published Date

  • August 13, 2017

Published In

  • Proceedings of the Acm Sigkdd International Conference on Knowledge Discovery and Data Mining

Volume / Issue

  • Part F129685 /

Start / End Page

  • 35 - 44

International Standard Book Number 13 (ISBN-13)

  • 9781450348874

Digital Object Identifier (DOI)

  • 10.1145/3097983.3098047

Citation Source

  • Scopus