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Arrogance in classification

AbstructThe Department of Defense and the Department of the Air Force have funded automatic target recognition for several decades with varied success. The foundation of automatic target recognition is based upon pattern recognition. In this paper, we present new pattern recognition conceptsspecifically in the area of classification-and propose a new visualization technique that allows one to determine when a classifier is being arrogant. Clearly, arrogance in classification is an undesirable attribute. A human is being arrogant when their expressed conviction in a decision overstates their actual experience in making similar decisions. Likewise, given an input feature vector we say a classifier is arrogant in its classification if its veracity is high yet its experience is low. Conversely, a classifier is non-arrogant in its classification if there is a reasonable balance between its veracity and its experience. It is possible to quantify this balance and we discuss new techniques that will detect arrogance in a classifier.