Cost-Sensitive Learning

Description

In classical machine learning or data mining settings, the classifiers usually try to minimize the number of errors they will make in dealing with new data. Such a setting is valid only when the costs of different errors are equal. Unfortunately, in many real-world applications the costs of different errors are often unequal. For example, in medical diagnosis, the cost of erroneously diagnosing a patient to be healthy may be much bigger than that of mistakenly diagnosing a healthy person as being sick, because the former kind of error may result in the loss of a life.

Previous Workshops

• 2nd Workshop on Utility-Based Data Mining (held with KDD'2006)

Our goal is to promote an examination of all of the utility factors that affect data mining and their interaction, in order to continue to encourage the field to go beyond what has been accomplished individually in the areas of active learning and cost-sensitive learning. In addition, this workshop will continue to explore the types of utility factors and new methods for incorporating utility considerations in both predictive and descriptive data mining tasks. We welcome recent work on Value of Information analysis over graphical models.  

• 1st Workshop on Utility-Based Data Mining (held with KDD'2005)
• Workshop on Cost-Sensitive Learning (held with ICML'2000)

Special Issue

• A special issue of the Data Mining and Knowledge Discovery Journal on Utility-Based Data Mining

Paper List

• P. Turney. Types of cost in inductive learning.

• C. Elkan. The foundation of cost-sensitive learning.

A Cost-Sensitive Learning Bibliography (papers ranged in category)
Cost-Sensitive Learning Bibliography (by Peter Turney)

Researchers in This Field

• Naoki Abe, IBM, T.J. Watson

• ... ...

Last Modified: 2007-04-05 by Xu-Ying Liu

Machine Learning Topics

Cost-Sensitive Learning

Imbalance Problem

Rare Event Detection

ROC Analysis