Learning cost-sensitive active classifiers☆☆This extends the short conference paper [19].
نویسندگان
چکیده
منابع مشابه
Active Cost-Sensitive Learning
For many classification tasks a large number of instances available for training are unlabeled and the cost associated with the labeling process varies over the input space. Meanwhile, virtually all these problems require classifiers that minimize a nonuniform loss function associated with the classification decisions (rather than the accuracy or number of errors). For example, to train pattern...
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In active learning, one attempts to maximize classifier performance for a given number of labeled training points by allowing the active learning algorithm to choose which points should be labeled. Typically, when the active learner requests labels for the selected points, it assumes that all points require the same amount of effort to label and that the cost of labeling a point is independent ...
متن کاملLearning cost-sensitive active classifiers
Most classification algorithms are “passive”, in that they assign a class label to each instance based only on the description given, even if that description is incomplete. By contrast, an active classifier can — at some cost — obtain the values of some unspecified attributes, before deciding upon a class label. This can be useful, for instance, when deciding whether to gather information rele...
متن کاملActive Learning for Cost-Sensitive Classification
We design an active learning algorithm for cost-sensitive multiclass classification: problems where different errors have different costs. Our algorithm, COAL, makes predictions by regressing to each label’s cost and predicting the smallest. On a new example, it uses a set of regressors that perform well on past data to estimate possible costs for each label. It queries only the labels that cou...
متن کاملActive Learning for Cost-Sensitive Classification
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 correspon...
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ژورنال
عنوان ژورنال: Artificial Intelligence
سال: 2002
ISSN: 0004-3702
DOI: 10.1016/s0004-3702(02)00209-6