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Cost-Sensitive Learning

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Encyclopedia of Machine Learning and Data Mining

Synonyms

Cost-sensitive classification; Learning with different classification costs

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

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Author information

Authors and Affiliations

  1. The University of Western Ontario, London, ON, Canada

    Charles X. Ling & Victor S. Sheng

Authors
  1. Charles X. Ling
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  2. Victor S. Sheng
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Editor information

Editors and Affiliations

  1. The University of New South Wales, Sydney, NSW, Australia

    Claude Sammut

  2. Faculty of Information Technology, Monash University, Melbourne, VIC, Australia

    Geoffrey I. Webb

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Ling, C.X., Sheng, V.S. (2017). Cost-Sensitive Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_181

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