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Learning certifiably optimal rule lists

Publication ,  Conference
Angelino, E; Larus-Stone, N; Alabi, D; Seltzer, M; Rudin, C
Published in: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
August 13, 2017

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.

Duke Scholars

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Published In

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

DOI

ISBN

9781450348874

Publication Date

August 13, 2017

Volume

Part F129685

Start / End Page

35 / 44
 

Citation

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Angelino, E., Larus-Stone, N., Alabi, D., Seltzer, M., & Rudin, C. (2017). Learning certifiably optimal rule lists. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Vol. Part F129685, pp. 35–44). https://doi.org/10.1145/3097983.3098047
Angelino, E., N. Larus-Stone, D. Alabi, M. Seltzer, and C. Rudin. “Learning certifiably optimal rule lists.” In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Part F129685:35–44, 2017. https://doi.org/10.1145/3097983.3098047.
Angelino E, Larus-Stone N, Alabi D, Seltzer M, Rudin C. Learning certifiably optimal rule lists. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017. p. 35–44.
Angelino, E., et al. “Learning certifiably optimal rule lists.” Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. Part F129685, 2017, pp. 35–44. Scopus, doi:10.1145/3097983.3098047.
Angelino E, Larus-Stone N, Alabi D, Seltzer M, Rudin C. Learning certifiably optimal rule lists. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017. p. 35–44.

Published In

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

DOI

ISBN

9781450348874

Publication Date

August 13, 2017

Volume

Part F129685

Start / End Page

35 / 44