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