Learning Active Classifiers

机译：学习主动分类器

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Many 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. In contrast, an active classifier can - at some cost - obtain the values of missing attributes, before deciding upon a class label. The expected utility of using an active classifier depends on both the cost required to obtain the additional attribute values and the penalty incurred if it outputs the wrong classification. This paper considers the problem of learning near-optimal active classifiers, using a variant of the probably-approximately-correct (PAC) model. After defining the framework - which is perhaps the main contribution of this paper - we describe a situation where this task can be achieved efficiently, but then show that the task is often intractable.

机译：许多分类算法是“被动的”，因为它们仅基于给定的描述为每个实例分配一个类别标签，即使该描述不完整。相反，主动分类器可以在决定类标签之前以某种代价获得缺失属性的值。使用主动分类器的预期效用取决于获得附加属性值所需的成本以及输出错误分类时产生的损失。本文考虑了使用近似正确校正（PAC）模型的变体来学习接近最优的主动分类器的问题。在定义了框架（这可能是本文的主要贡献）之后，我们描述了可以有效完成此任务的情况，但随后表明该任务通常很棘手。

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来源
《Machine learning》|1996年|207-215|共9页
会议地点 Bari(IT);Bari(IT)
作者
Russell Greiner; Adam J. Grove; Dan Roth;
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作者单位

Siemens Corporate Research 755 College Road East Princeton, NJ 08540-6632;

NEC Research Institute 4 Independence Way Princeton, NJ 08540;

Dept. of Appl. Math. CS Weizmann Institute of Science Rehovot 76100, Israel;

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正文语种 eng
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Learning Active Classifiers

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