Encyclopedia of Machine Learning and Data Mining

2017 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Cost-Sensitive Learning

  • Charles X. Ling
  • Victor S. Sheng
Reference work entry
DOI: https://doi.org/10.1007/978-1-4899-7687-1_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 CX (2004) Test-cost sensitive naïve Bayesian classification. In: Proceedings of the fourth IEEE international conference on data mining. IEEE Computer Society Press, BrightonGoogle Scholar
  2. Domingos P (1999) MetaCost: a general method for making classifiers cost-sensitive. In: Proceedings of the fifth international conference on knowledge discovery and data mining, San Diego. ACM, New York, pp 155–164Google Scholar
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  4. Elkan C (2001) The foundations of cost-sensitive learning. In: Proceedings of the 17th international joint conference of artificial intelligence. Morgan Kaufmann, Seattle, pp 973–978Google Scholar
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  6. Ling CX, Yang Q, Wang J, Zhang S (2004) Decision trees with minimal costs. In: Proceedings of 2004 international conference on machine learning (ICML’2004), BanffGoogle Scholar
  7. Sheng VS, Ling CX (2006) Thresholding for making classifiers cost-sensitive. In: Proceedings of the 21st national conference on artificial intelligence, 16–20 July 2006, Boston, pp 476–481Google Scholar
  8. Ting KM (1998) Inducing cost-sensitive trees via instance weighting. In: Proceedings of the second European symposium on principles of data mining and knowledge discovery. Springer, Heidelberg, pp 23–26Google Scholar
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  10. Turney PD (2000) Types of cost in inductive concept learning. In: Proceedings of the workshop on cost-sensitive learning at the 17th international conference on machine learning, Stanford University, StanfordGoogle Scholar
  11. Witten IH, Frank E (2005) Data mining – practical machine learning tools and techniques with Java implementations. Morgan Kaufmann, San FranciscozbMATHGoogle Scholar
  12. Zadrozny B, Elkan C (2001) Learning and making decisions when costs and probabilities are both unknown. In: Proceedings of the seventh international conference on knowledge discovery and data mining, pp 204–213Google Scholar
  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, MelbourneGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Charles X. Ling
    • 1
  • Victor S. Sheng
    • 1
  1. 1.The University of Western OntarioLondonCanada