Automatic Feature Evaluator (AFE)

"This proposal attacks the Navy clustering problem by first dividing the reported features into two classes: primary features (those intended to be useful) and secondary features (those unintentionally useful). Subject matter experts will then explain howthey have used the secondary features to form initial clusters of primary features. An expert system based upon the human experts will be developed and iteratively combined with statistics such as a modified Bayesian statistic to estimate the number ofclusters, eigenvectors to estimate the number of dimensions, and a modified F-ratio to estimate the strength of each feature. The result will be an estimate of which features to use and how to use them for the new class, and will provide an initial set ofclusters. This development would have very broad applicability to commercial systems that need to operate in real time based upon inputs that are varied in type and quality."