In this thesis, we address the problem of learning the Markov blanket of a quantity from data in an efficient manner. The discovery of the Markov blanket is useful in the feature subset selection problem and in discovering the structure of a Bayesian network. Our contribution is a novel algorithm called FAST-IAMB for the induction of Markov blankets that employs a fast heuristic to quickly converge to the Markov blanket. Empirical results show that the algorithm performs reasonably faster than existing algorithms. In addition, the results also show that the speedup does not adversely affect the accuracy of the recovered Markov blankets
