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...
Recommended Reading
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.
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 (pp. 155–164). New York: ACM.
Drummond, C., & Holte, R. (2000). Exploiting the cost (in)sensitivity of decision tree splitting criteria. In Proceedings of the 17th international conference on machine learning (pp. 239–246).
Elkan, C. (2001). The foundations of cost-sensitive learning. In Proceedings of the 17th international joint conference of artificial intelligence (pp. 973–978). Seattle: Morgan Kaufmann.
Japkowicz, N., & Stephen, S. (2002). The class imbalance problem: A systematic study. Intelligent Data Analysis, 6(5), 429–450.
Ling, C. X., Yang, Q., Wang, J., & Zhang, S. (2004). Decision trees with minimal costs. InProceedings of 2004 international conference on machine learning (ICML’2004).
Sheng, V. S., & Ling, C. X. (2006). Thresholding for making classifiers cost-sensitive. In Proceedings of the 21st national conference on artificial intelligence (pp. 476–481), 16–20 July 2006, Boston, Massachusetts.
Ting, K. M. (1998). Inducing cost-sensitive trees via instance weighting. In Proceedings of the second European symposium on principles of data mining and knowledge discovery (pp. 23–26). Heidelberg: Springer.
Turney, P. D. (1995). Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm. Journal of Artificial Intelligence Research, 2, 369–409.
Turney, P. D. (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, California.
Witten, I. H., & Frank, E. (2005). Data mining – Practical machine learning tools and techniques with Java implementations. San Francisco: Morgan Kaufmann.
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. 204–213).
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.
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Ling, C.X., Sheng, V.S. (2011). Cost-Sensitive Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_181
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DOI: https://doi.org/10.1007/978-0-387-30164-8_181
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