TY - UNPB ID - cogprints1804 UR - http://cogprints.org/1804/ A1 - Turney, Peter Y1 - 2000/// N2 - Inductive 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. KW - cost KW - learning KW - misclassification error KW - inductive concept learning KW - complexity KW - cost-sensitive learning. TI - Types of cost in inductive concept learning SP - 15 AV - public EP - 21 ER -