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We present a machine learning algorithm for building classifiers that arecomprised of a small number of disjunctions of conjunctions (or's of and's). Anexample 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 wepredict that Y=1, ELSE predict Y=0. An attribute-value pair is called a literaland a conjunction of literals is called a pattern. Models of this form have theadvantage of being interpretable to human experts, since they produce a set ofconditions that concisely describe a specific class. We present twoprobabilistic models for forming a pattern set, one with a Beta-Binomial prior,and the other with Poisson priors. In both cases, there are prior parametersthat 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 providetwo scalable MAP inference approaches: a pattern level search, which involvesassociation rule mining, and a literal level search. We show stronger priorsreduce computation. We apply the Bayesian Or's of And's (BOA) model to predictuser behavior with respect to in-vehicle context-aware personalized recommendersystems.

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