Types of Cost in Inductive Concept Learning

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TypeArticle
ConferenceProceedings of the Cost-Sensitive Learning Workshop at the 17th ICML-2000 Conference, July 2, 2000., Stanford, California, USA
AbstractInductive 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.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number43671
NPARC number5755274
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Record identifier88be18fa-23f3-47bf-a765-057e2dcf7bb8
Record created2008-12-02
Record modified2016-05-09
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