Math @ Duke

Publications [#330622] of Cynthia D. Rudin
Papers Published
 Wang, T; Rudin, C; DoshiVelez, F; Liu, Y; Klampfl, E; MacNeille, P, A Bayesian framework for learning rule sets for interpretable classification,
Journal of Machine Learning Research, vol. 18
(August, 2017),
pp. 137
(last updated on 2018/10/19)
Abstract: ©2017 Tong Wang, Cynthia Rudin, Finale DoshiVelez, Yimin Liu, Erica Klampfl, and Perry MacNeille. We present a machine learning algorithm for building classifiers that are comprised of a small number of short rules. These are restricted disjunctive normal form models. An example of a classifier of this form is as follows: If X satisfies (condition A AND condition B) OR (condition C) OR · · · , then Y = 1. Models of this form have the advantage of being interpretable to human experts since they produce a set of rules that concisely describe a specific class. We present two probabilistic models with prior parameters that the user can set to encourage the model to have a desired size and shape, to conform with a domainspecific definition of interpretability. We provide a scalable MAP inference approach and develop theoretical bounds to reduce computation by iteratively pruning the search space. We apply our method (Bayesian Rule Sets – BRS) to characterize and predict user behavior with respect to invehicle contextaware personalized recommender systems. Our method has a major advantage over classical associative classification methods and decision trees in that it does not greedily grow the model.


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