Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems
arXiv:1504.07614
Abstract
We present a machine learning algorithm for building classifiers that are comprised of a small number of disjunctions of conjunctions (or's of and's). An example of a classifier of this form is as follows: If X satisfies (x1 = 'blue' AND x3 = 'middle') OR (x1 = 'blue' AND x2 = '<15') OR (x1 = 'yellow'), then we predict that Y=1, ELSE predict Y=0. An attribute-value pair is called a literal and a conjunction of literals is called a pattern. Models of this form have the advantage of being interpretable to human experts, since they produce a set of conditions that concisely describe a specific class. We present two probabilistic models for forming a pattern set, one with a Beta-Binomial prior, and the other with Poisson priors. In both cases, there are 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 two scalable MAP inference approaches: a pattern level search, which involves association rule mining, and a literal level search. We show stronger priors reduce computation. We apply the Bayesian Or's of And's (BOA) model to predict user behavior with respect to in-vehicle context-aware personalized recommender systems.
References in corpus (3)
Cited by in corpus (9)
- A Survey on the Explainability of Supervised Machine Learning
- Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
- Learning Certifiably Optimal Rule Lists for Categorical Data
- MLIC: A MaxSAT-Based framework for learning interpretable classification rules
- Interpretable Two-level Boolean Rule Learning for Classification
- Learning Optimized Or's of And's
- Scalable Bayesian Rule Lists
- Concave Quadratic Cuts for Mixed-Integer Quadratic Problems
- FLAME: A Self-Adaptive Auto-labeling System for Heterogeneous Mobile Processors