Falling Rule Lists

Fulton Wang, Cynthia Rudin
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, PMLR 38:1013-1022, 2015.

Abstract

Falling rule lists are classification models consisting of an ordered list of if-then rules, where (i) the order of rules determines which example should be classified by each rule, and (ii) the estimated probability of success decreases monotonically down the list. These kinds of rule lists are inspired by healthcare applications where patients would be stratified into risk sets and the highest at-risk patients should be considered first. We provide a Bayesian framework for learning falling rule lists that does not rely on traditional greedy decision tree learning methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v38-wang15a, title = {{Falling Rule Lists}}, author = {Wang, Fulton and Rudin, Cynthia}, booktitle = {Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics}, pages = {1013--1022}, year = {2015}, editor = {Lebanon, Guy and Vishwanathan, S. V. N.}, volume = {38}, series = {Proceedings of Machine Learning Research}, address = {San Diego, California, USA}, month = {09--12 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v38/wang15a.pdf}, url = {https://proceedings.mlr.press/v38/wang15a.html}, abstract = {Falling rule lists are classification models consisting of an ordered list of if-then rules, where (i) the order of rules determines which example should be classified by each rule, and (ii) the estimated probability of success decreases monotonically down the list. These kinds of rule lists are inspired by healthcare applications where patients would be stratified into risk sets and the highest at-risk patients should be considered first. We provide a Bayesian framework for learning falling rule lists that does not rely on traditional greedy decision tree learning methods.} }
Endnote
%0 Conference Paper %T Falling Rule Lists %A Fulton Wang %A Cynthia Rudin %B Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2015 %E Guy Lebanon %E S. V. N. Vishwanathan %F pmlr-v38-wang15a %I PMLR %P 1013--1022 %U https://proceedings.mlr.press/v38/wang15a.html %V 38 %X Falling rule lists are classification models consisting of an ordered list of if-then rules, where (i) the order of rules determines which example should be classified by each rule, and (ii) the estimated probability of success decreases monotonically down the list. These kinds of rule lists are inspired by healthcare applications where patients would be stratified into risk sets and the highest at-risk patients should be considered first. We provide a Bayesian framework for learning falling rule lists that does not rely on traditional greedy decision tree learning methods.
RIS
TY - CPAPER TI - Falling Rule Lists AU - Fulton Wang AU - Cynthia Rudin BT - Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics DA - 2015/02/21 ED - Guy Lebanon ED - S. V. N. Vishwanathan ID - pmlr-v38-wang15a PB - PMLR DP - Proceedings of Machine Learning Research VL - 38 SP - 1013 EP - 1022 L1 - http://proceedings.mlr.press/v38/wang15a.pdf UR - https://proceedings.mlr.press/v38/wang15a.html AB - Falling rule lists are classification models consisting of an ordered list of if-then rules, where (i) the order of rules determines which example should be classified by each rule, and (ii) the estimated probability of success decreases monotonically down the list. These kinds of rule lists are inspired by healthcare applications where patients would be stratified into risk sets and the highest at-risk patients should be considered first. We provide a Bayesian framework for learning falling rule lists that does not rely on traditional greedy decision tree learning methods. ER -
APA
Wang, F. & Rudin, C.. (2015). Falling Rule Lists. Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 38:1013-1022 Available from https://proceedings.mlr.press/v38/wang15a.html.

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