Types of Cost in Inductive Concept Learning

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dc.creatorTurney, Peter D.
dc.date2002-12-11
dc.date.accessioned2026-07-07T03:19:16Z
dc.date.available2026-07-07T03:19:16Z
dc.descriptionInductive concept learning is the task of learning to assign cases to a discrete set of classes. In real-world applications of concept learning, there are many different types of cost involved. The majority of the machine learning literature ignores all types of cost (unless accuracy is interpreted as a type of cost measure). A few papers have investigated the cost of misclassification errors. Very few papers have examined the many other types of cost. In this paper, we attempt to create a taxonomy of the different types of cost that are involved in inductive concept learning. This taxonomy may help to organize the literature on cost-sensitive learning. We hope that it will inspire researchers to investigate all types of cost in inductive concept learning in more depth.
dc.description7 pages
dc.identifierhttps://arxiv.org/abs/cs/0212034
dc.identifierhttp://arxiv.org/abs/cs/0212034
dc.identifierWorkshop on Cost-Sensitive Learning at the Seventeenth International Conference on Machine Learning, (2000), Stanford University, California, 15-21
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31393
dc.subjectMachine Learning
dc.subjectComputer Vision and Pattern Recognition
dc.subjectI.2.6; I.5.2
dc.titleTypes of Cost in Inductive Concept Learning
dc.typetext

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