Automatic Feature Evaluator (AFE)

The Automatic Feature Evaluator (AFE) Phase II program will develop a demonstration capability to show how data with different, missing, and corrupted attributes can be assembled into a decision-making process. The unit will address three areas ofconcern: clustering, the formation of some initial groupings (clusters) of measurements, each representing an object; classification, the subsequent evolution of the set of clusters as new reports come in and are assigned to clusters; and maintenance, theroutine and non-routine analysis of the cluster space to detect and correct problems. Although the algorithms are in general statistical in nature, they do not assume any particular distribution of the elements reported. The algorithms are able to dealwith measurements that are non-ideal in other ways also. They can handle elements that are discrete and even non-numeric. They can deal with reports that contain missing data, outliers, or gross errors. They can also handle multi-modal distributions andare able to track changes in the underlying distributions over time. Some of these issues are addressed on the basis of knowledge of the reports and their content, but most of the issues are addressed in general terms. This technology solves the problem ofusing very different data inputs to derive grouping and classification solutions