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.

该文介绍了一种机器学习算法，用于构建包含少量析取合取式（或代数乘积的或）的分类器，并提供了两种概率模型以便形成模式集合。同时，他们也提供了两种可扩展的 MAP 推断方法，这些推断方法有助于预测有关车载上下文感知个性化推荐系统的用户行为。