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Types of cost in inductive concept learning

Turney, Peter (2000) Types of cost in inductive concept learning. [Conference Paper] (Unpublished)

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Abstract

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.

Item Type:Conference Paper
Keywords:cost, learning, misclassification error, inductive concept learning, complexity, cost-sensitive learning.
Subjects:Computer Science > Artificial Intelligence
Computer Science > Machine Learning
Computer Science > Statistical Models
ID Code:1804
Deposited By: Turney, Peter
Deposited On:17 Sep 2001
Last Modified:11 Mar 2011 08:54

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