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  -

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