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Publications [#330621] of Cynthia D. Rudin

Papers Published

  1. 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, vol. Part F129685 (August, 2017), pp. 35-44, ISBN 9781450348874 [doi]
    (last updated on 2018/04/26)

    © 2017 Copyright held by the owner/author(s). 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.
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