  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.
