Journal article
A Bayesian Framework for Learning Rule Sets for Interpretable Classification
Journal of machine learning research, Vol.18, pp.1-37
08/01/2017
Abstract
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.
Details
- Title: Subtitle
- A Bayesian Framework for Learning Rule Sets for Interpretable Classification
- Creators
- Tong Wang - Univ Iowa, Iowa City, IA 52242 USACynthia Rudin - Duke UniversityFinale Doshi-Velez - Harvard UniversityYimin Liu - Edward Jones, St Louis, MO USAErica Klampfl - Ford Motor Co, Dearborn, MI 48121 USAPerry MacNeille - Ford Motor Co, Dearborn, MI 48121 USA
- Resource Type
- Journal article
- Publication Details
- Journal of machine learning research, Vol.18, pp.1-37
- Publisher
- Microtome Publ
- ISSN
- 1532-4435
- eISSN
- 1533-7928
- Number of pages
- 37
- Language
- English
- Date published
- 08/01/2017
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380445402771
Metrics
2 Record Views