Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems

TL;DR

This paper introduces a probabilistic, interpretable classification model called Bayesian Or's of And's, which uses logical patterns for transparent decision-making, and demonstrates its application in context-aware recommender systems.

Contribution

The paper proposes a novel Bayesian framework for building interpretable classifiers based on logical patterns, with scalable inference methods and application to recommender systems.

Findings

Stronger priors improve computational efficiency.

The model achieves interpretable classification with competitive accuracy.

Effective in predicting user behavior in context-aware systems.

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.