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Bayesian rule sets for interpretable classification

Publication ,  Conference
Wang, T; Rudin, C; Velez-Doshi, F; Liu, Y; Klampfl, E; Macneille, P
Published in: Proceedings - IEEE International Conference on Data Mining, ICDM
July 2, 2016

A Rule Set model consists of a small number of short rules for interpretable classification, where an instance is classified as positive if it satisfies at least one of the rules. The rule set provides reasons for predictions, and also descriptions of a particular class. We present a Bayesian framework for learning Rule Set models, with prior parameters that the user can set to encourage the model to have a desired size and shape in order to conform with a domain-specific definition of interpretability. We use an efficient inference approach for searching for the MAP solution and provide theoretical bounds to reduce computation. We apply Rule Set models to ten UCI data sets and compare the performance with other interpretable and non-interpretable models.

Duke Scholars

Published In

Proceedings - IEEE International Conference on Data Mining, ICDM

DOI

ISSN

1550-4786

Publication Date

July 2, 2016

Volume

0

Start / End Page

1269 / 1274
 

Citation

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Wang, T., Rudin, C., Velez-Doshi, F., Liu, Y., Klampfl, E., & Macneille, P. (2016). Bayesian rule sets for interpretable classification. In Proceedings - IEEE International Conference on Data Mining, ICDM (Vol. 0, pp. 1269–1274). https://doi.org/10.1109/ICDM.2016.130
Wang, T., C. Rudin, F. Velez-Doshi, Y. Liu, E. Klampfl, and P. Macneille. “Bayesian rule sets for interpretable classification.” In Proceedings - IEEE International Conference on Data Mining, ICDM, 0:1269–74, 2016. https://doi.org/10.1109/ICDM.2016.130.
Wang T, Rudin C, Velez-Doshi F, Liu Y, Klampfl E, Macneille P. Bayesian rule sets for interpretable classification. In: Proceedings - IEEE International Conference on Data Mining, ICDM. 2016. p. 1269–74.
Wang, T., et al. “Bayesian rule sets for interpretable classification.” Proceedings - IEEE International Conference on Data Mining, ICDM, vol. 0, 2016, pp. 1269–74. Scopus, doi:10.1109/ICDM.2016.130.
Wang T, Rudin C, Velez-Doshi F, Liu Y, Klampfl E, Macneille P. Bayesian rule sets for interpretable classification. Proceedings - IEEE International Conference on Data Mining, ICDM. 2016. p. 1269–1274.

Published In

Proceedings - IEEE International Conference on Data Mining, ICDM

DOI

ISSN

1550-4786

Publication Date

July 2, 2016

Volume

0

Start / End Page

1269 / 1274