Data sets with many discrete variables and relatively few cases arise in health care, ecommerce, information security, text mining, and many other domains. Learning effective and efficient prediction models from such data sets is a challenging task. In this paper, we propose a Tabu Search enhanced Markov Blanket (TS/MB) procedure to learn a graphical Markov Blanket classifier from data. The TS/MB procedure is based on the use of restricted neighborhoods in a general Bayesian Network constrained by the Markov condition, called Markov Blanket Neighborhoods. Computational results from real world data sets drawn from several domains indicate that the TS/MB procedure is able to find a parsimonious model with substantially fewer predictor variables than in the full data set, and provides comparable prediction performance when compared against several machine learning methods.

53 pages

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