Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Cost-Sensitive Learning

  • Charles X. Ling
  • Victor S. Sheng
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_181

Synonyms

Definition

Cost-Sensitive Learning is a type of learning that takes the misclassification costs (and possibly other types of cost) into consideration. The goal of this type of learning is to minimize the total cost. The key difference between cost-sensitive learning and cost-insensitive learning is that cost-sensitive learning treats different misclassifications differently. That is, the cost for labeling a positive example as negative can be different from the cost for labeling a negative example as positive. Cost-insensitive learning does not take misclassification costs into consideration.

Motivation and Background

Classification is an important task in inductive learning and machine learning. A classifier, trained from a set of training examples with class labels, can then be used to predict the class labels of new examples. The class label is usually discrete and finite. Many effective...

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Recommended Reading

  1. Chai, X., Deng, L., Yang, Q., & Ling, C. X. (2004). Test-cost sensitive naïve Bayesian classification. In Proceedings of the fourth IEEE international conference on data mining. Brighton: IEEE Computer Society Press.Google Scholar
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  13. Zadrozny, B., Langford, J., & Abe, N. (2003). Cost-sensitive learning by cost-proportionate instance weighting. In Proceedings of the third International conference on data mining.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Charles X. Ling
  • Victor S. Sheng

There are no affiliations available