Active Learning for Cost-Sensitive Classification
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چکیده
i,y with features x and label y. The computation of this sensitivity value is governed by the actual online update where we compute the derivative of the change in the prediction as a function of the importance weight w for a hypothetical example with cost 0 or cost 1 and the same features. This is possible for essentially all online update rules on importance weighted examples and it corresponds to taking the limit as w ! 0 of the change in prediction due to an update divided by w. By inspection this requires only O(d) time per example, where d is the average number of non-zero features. With these two steps, we obtain approximate minimum and maximum costs using go
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