Falling Rule Lists
Artificial Intelligence
2015-02-03 v3 Machine Learning
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
@article{arxiv.1411.5899,
title = {Falling Rule Lists},
author = {Fulton Wang and Cynthia Rudin},
journal= {arXiv preprint arXiv:1411.5899},
year = {2015}
}
Comments
Accepted at AISTATS 2015. Contains number of rules mined, running times. in Proceedings of AISTATS 2015. JMLR: W&CP 38