I. Introduction
Machine learning (ML) methods play a central role in the solution of many security problems, including cyber defense, transportation security, counterterrorism, and crime prevention [e.g. [1]-[3]. For instance, ML techniques enable malicious and innocent activities to be rapidly and accurately distinguished, and appropriate actions to be taken, even when the patterns associated with these activities are buried in large, heterogeneous datasets. Roughly speaking, ML algorithms automatically learn relationships between observed variables from examples presented in the form of training data; the learned relationships are then used to generate predictions in new situations, i.e., for the test data [4]. ML's capacity to learn from examples, scale to large datasets, and adapt to new conditions make this an attractive approach to predictive analytics in general and for security informatics in particular.