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A Bayesian framework for learning rule sets for interpretable classification

Publication ,  Journal Article
Wang, T; Rudin, C; Doshi-Velez, F; Liu, Y; Klampfl, E; MacNeille, P
Published in: Journal of Machine Learning Research
August 1, 2017

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 domain-specific 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 in-vehicle context-aware 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.

Duke Scholars

Published In

Journal of Machine Learning Research

EISSN

1533-7928

ISSN

1532-4435

Publication Date

August 1, 2017

Volume

18

Start / End Page

1 / 37

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4905 Statistics
  • 4611 Machine learning
  • 17 Psychology and Cognitive Sciences
  • 08 Information and Computing Sciences
 

Citation

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Wang, T., Rudin, C., Doshi-Velez, F., Liu, Y., Klampfl, E., & MacNeille, P. (2017). A Bayesian framework for learning rule sets for interpretable classification. Journal of Machine Learning Research, 18, 1–37.
Wang, T., C. Rudin, F. Doshi-Velez, Y. Liu, E. Klampfl, and P. MacNeille. “A Bayesian framework for learning rule sets for interpretable classification.” Journal of Machine Learning Research 18 (August 1, 2017): 1–37.
Wang T, Rudin C, Doshi-Velez F, Liu Y, Klampfl E, MacNeille P. A Bayesian framework for learning rule sets for interpretable classification. Journal of Machine Learning Research. 2017 Aug 1;18:1–37.
Wang, T., et al. “A Bayesian framework for learning rule sets for interpretable classification.” Journal of Machine Learning Research, vol. 18, Aug. 2017, pp. 1–37.
Wang T, Rudin C, Doshi-Velez F, Liu Y, Klampfl E, MacNeille P. A Bayesian framework for learning rule sets for interpretable classification. Journal of Machine Learning Research. 2017 Aug 1;18:1–37.

Published In

Journal of Machine Learning Research

EISSN

1533-7928

ISSN

1532-4435

Publication Date

August 1, 2017

Volume

18

Start / End Page

1 / 37

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4905 Statistics
  • 4611 Machine learning
  • 17 Psychology and Cognitive Sciences
  • 08 Information and Computing Sciences