We have developed efficient methods to score structured hypotheses from technologies that fuse evidence from massive data streams to detect threat phenomena. We have generalized metrics (precision, recall, F-value, and area under the precision-recall curve) traditionally used in the information retrieval and machine learning communities to realize object-oriented versions that accommodate inexact matching over structured hypotheses with weighted attributes. We also exploit the object-oriented precision and recall metrics in additional metrics that account for the costs of false-positive and false-negative threat reporting. We have reported on our scoring methods more fully previously; the present brief presentation is offered to help make this work accessible to the machine learning community.
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