title: Types of cost in inductive concept learning
creator: Turney, Peter
subject: Artificial Intelligence
subject: Machine Learning
subject: Statistical Models
description: 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. 
date: 2000
type: Conference Paper
type: NonPeerReviewed
format: application/postscript
identifier: http://cogprints.org/1804/1/cost-sensitive.ps
format: application/pdf
identifier: http://cogprints.org/1804/5/cost-sensitive.pdf
identifier:   Turney, Peter  (2000) Types of cost in inductive concept learning.  [Conference Paper]    (Unpublished)  
relation: http://cogprints.org/1804/