We introduce cost-sensitive regression as a way to introduce information obtained by planning as background knowledge into a relational reinforcement learning algorithm. By offering a trade-off between using knowledge rich, but computationally expensive knowledge resulting from planning like approaches such as minimax search and computationally cheap, but possibly incorrect generalizations, the...

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...

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 ...

This chapter introduces cost-sensitive learning and its importance in medicine. Health managers and clinicians often need models that try to minimize several types of costs associated with healthcare, including attribute costs (e.g. the cost of a specific diagnostic test) and misclassification costs (e.g. the cost of a false negative test). In fact, as in other professional areas, both diagnost...

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...