Abstract
A procedure is introduced to approximate nearest neighbor (INN) decision boundaries. The algorithm produces a selective subset of the original data so that 1) the subset is consistent, 2) the distance between any sample and its nearest selective neighbor is less than the distance from the sample to any sample of the other class, and 3) the subset is the smallest possible.
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