U.S. Department of Energy

Pacific Northwest National Laboratory

Signature Construction

Signature construction is the creation of a classifier that maps the feature vector of each observational unit to a set of labels. The term classifier refers to any type of classification method, statistical model, or machine learning algorithm that maps features to labels. Labels can be a discrete set (e.g., Threat, Not a Threat) or a quantitative interval (e.g., predicting the mass of an object).  Ideally, each prediction also provides a measure of uncertainty.

There are two steps in constructing the classifier:

  1. Train the classifier
    • Estimate classifier parameters by optimizing one or more objective functions.
  2. Test the classifier
    • Use the random subsets of data not used for training a particular instance of the classifier to test the fidelity of the classifier predictions.
    • The measure of classifier fidelity will likely apply principles from "Signature quality assessment" (see below).
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