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
Published In
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
DOI
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
Publication Date
August 13, 2017
Volume
Part F129685
Start / End Page
35 / 44