A Bayesian Framework for Learning Rule Sets for Interpretable Classification

作者：Wang Tong^*; Rudin Cynthia; Doshi Velez Finale; Liu Yimin; Klampfl Erica; MacNeille Perry

来源：Journal of Machine Learning Research, 2017, 18: 1-37.

• disjunctive normal form
• statistical learning
• data mining
• association rules
• interpretable classifier
• Bayesian modeling

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.

• 出版日期2017