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Learning cost-sensitive active classifiers

Learning cost-sensitive active classifiers,10.1016/S0004-3702(02)00209-6,Artificial Intelligence,Russell Greiner,Adam J. Grove,Dan Roth

Learning cost-sensitive active classifiers   (Citations: 60)
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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 relevant to a medical procedure or experiment. The expected utility of using an active classifier depends on both the cost required to obtain the values of additional attributes and the penalty incurred if the classifier outputs the wrong classification. This paper analyzes the problem of learning optimal active classifiers, using a variant of the probably-approximately-correct (PAC) model. After defining the framework, we show that this task can be achieved eciently when the active classifier is allowed to perform only (at most) a constant number of tests. We then show that, in more general environments, this task of learning optimal active classifiers is often intractable.
Journal: Artificial Intelligence - AI , vol. 139, no. 2, pp. 137-174, 2002
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    • ...Cost As touched upon in the introduction to this chapter, obtaining implicit feedback from user selections is cheaper than asking the user to explicitly rate an item [19]...

    Neil Rubenset al. Active Learning in Recommender Systems

    • ...Greiner et al. (2002) were pioneers in studying classifiers that actively decide what tests to administer...
    • ...An extension to the work on optimal bounded active classifiers (Greiner et al. 2002 )i s when tests have costs also during the learning phase...

    Saher Esmeiret al. Anytime learning of anycost classifiers

    • ...Budgeted learning for active classifiers, which work on constrained budgets while querying attributes on a test example, is explored in the work of Greiner et al. (2002) for medical diagnosis...

    Sudheendra Vijayanarasimhanet al. Cost-Sensitive Active Visual Category Learning

    • ...Meanwhile, current active selection approaches that do account for labeling cost lead to a myopic selection of a single request at a time [4, 16, 17, 18, 11]...
    • ...The authors of [17] explore a form of budgeted learning where one can interactively accumulate answers about a particular test example, repeatedly deciding if a further query is worthwhile before making a prediction...

    Sudheendra Vijayanarasimhanet al. Far-sighted active learning on a budget for image and video recognitio...

    • ...Recently, researchers started to consider both test cost and misclassification cost [1, 2, 8-10]...
    • ...Similar in the interest in constructing an optimal learner, Greiner et al. [9] studied the theoretical aspects of active learning with test costs using a PAC learning framework...

    Zhenxing Qinet al. Cost Sensitive Classification in Data Mining

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