Naturally Constrained Reject Option Classification

We present the journal extension of our reject-option work. The method learns per-class softmax thresholds that maximize select accuracy while constraining the rejected region to behave like genuine correct-versus-incorrect confusion. Experiments span synthetic, image, and text classification, with separate reporting of select accuracy, reject accuracy, and coverage. The method does not require a user-specified rejection cost, target select accuracy, or target coverage, though its significance parameter still controls the operating tradeoff.