Constrained classification for infrastructure threat assessment

Validated computer simulation is an important aspect of critical infrastructure vulnerability assessment. The high computational cost of such models limits the number of threat scenarios that may be directly evaluated, which leads to a need for statistical emulation to predict outcomes for additional scenarios. Our particular area of interest is statistical methods for emulating complex computer codes that predict if a particular tunnel/explosive configuration results in the breaching of an underground transportation tunnel. In this case, there is considerable a priori information as to the properties of this breach classification boundary. We propose a constrained classifier, in the form of a parametric support vector machine, that allows us to incorporate expert knowledge into the shape of the decision boundary. We demonstrate the effectiveness of this technique with both a simulation study and by applying the method to a tunnel breach data set. This analysis reveals that constrained classification can offer substantial benefits for small sample sizes. The technique may be used either to provide a final classification result in the face of extremely limited data or as an interim step to guide adaptive sampling.