Under- and Overconfidence Sample Clauses

Under- and Overconfidence. A crucial point with the uncertainty estimates obtained from the classification is the question, how much one can rely on these estimates. Formally, our aim is to have a high correlation between prediction uncertainty and incorrectness of the classification (a similar idea was used by Xxxxx et al. [14]). In [12], this correlation was measured using the point- biserial correlation coefficient. However, this turns out to be too restrictive and only applicable in cases where the number of correctly and incorrectly classified samples is roughly balanced. Therefore, we take a different approach. For a test set X* of size K we define two functions u and o as follows: Kc x* еX* u( f , X*, Yˆ ) := 1 I(y* = yˆ)h( f (x*)) (3) correctly classified test samples, i.e. Kf + Kc = K. Thus, u is the average uncertainty of the correct classified samples and o is the average confidence of the incorrectly classified samples. We will denote u as the underconfidence and o as the overconfidence of the classifier. Intuitively, if all incorrect classified samples have the maximum confidence of 1 assigned, then the overconfidence reaches its maximum value of 1. This is the worst case for active learning, because the classifier is unable to give an indication that its predicted class label is wrong. As a consequence, the algorithm will never ask ground truth labels for the incorrectly classified samples, and they can not be used for re-training. Thus, the classification can not be improved. Another extreme case is that of maximal underconfidence
Sample 1
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Under- and Overconfidence. A crucial point with the uncertainty estimates obtained from the classification is the question, how much one can rely on these estimates. Formally, our aim is to have a high correlation between prediction uncertainty and incorrectness of the classification (a similar idea was used by Xxxxx et al. [14]). In [12], this correlation was measured using the point- biserial correlation coefficient. However, this turns out to be too restrictive and only applicable in cases where the number of correctly and incorrectly classified samples is roughly balanced. Therefore, we take a different approach. For a test set X× of size K we define two functions u and o as follows: Kc x× cX× u( f , X×, Yˆ ) := 1 . I(y× = yˆ)h( f (x×)) (3)
Sample 1

Related to Under- and Overconfidence

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