Cost Sensitive Classification in Data Mining

  • Zhenxing Qin
  • Chengqi Zhang
  • Tao Wang
  • Shichao Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6440)


Cost-sensitive classification is one of mainstream research topics in data mining and machine learning that induces models from data with unbalance class distributions and impacts by quantifying and tackling the unbalance. Rooted in diagnosis data analysis applications, there are great many techniques developed for cost-sensitive learning. They are mainly focused on minimizing the total cost of misclassification costs, test costs, or other types of cost, or a combination among these costs. This paper introduces the up-to-date prevailing cost-sensitive learning methods and presents some research topics by outlining our two new results: lazy-learning and semi-learning strategies for cost-sensitive classifiers.


Cost sensitive learning misclassification cost test cost 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Turney, P.: Types of cost in inductive concept learning. In: Workshop on Cost-Sensitive Learning at the Seventeenth International Conference on Machine Learning, pp. 15–11. Stanford University, California (2000)Google Scholar
  2. 2.
    Turney, P.D.: Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm. Journal of Artificial Intelligence Research 2, 369–409 (1995)Google Scholar
  3. 3.
    Elkan, C.: The foundations of cost-sensitive learning. In: Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, pp. 973–978. Morgan Kaufmann Publishers Inc., Seattle (2001)Google Scholar
  4. 4.
    Margineantu, D.D., Dietterich, T.G.: Improved class probability estimates from decision tree models. Lecture Notes in Statistics, vol. 171, pp. 169–184. Springer, New York (2002)zbMATHGoogle Scholar
  5. 5.
    Norton, S.W.: Generating better decision trees. In: Proceedings of the Eleventh International Conference on Artificial Intelligence, pp. 800–805. Morgan Kaufmann Publishers Inc., Detroit (1989)Google Scholar
  6. 6.
    Núñez, M.: The use of background knowledge in decision tree induction. Machine Learning 6(3), 231–250 (1991)Google Scholar
  7. 7.
    Tan, M.: Cost-sensitive learning of classification knowledge and its applications in robotics. Machine Learning 13(1), 7–33 (1993)Google Scholar
  8. 8.
    Zubek, V.B., Dietterich, T.G.: Pruning Improves Heuristic Search for Cost-Sensitive Learning. In: Proceedings of the Nineteenth International Conference on Machine Learning, pp. 27–34. Morgan Kaufmann Publishers Inc., San Francisco (2002)Google Scholar
  9. 9.
    Greiner, R., Grove, A.J., Roth, D.: Learning cost-sensitive active classifiers. Artificial Intelligence 139(2), 137–174 (2002)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Ling, C.X., Yang, Q., Wang, J., Zhang, S.: Decision trees with minimal costs. In: ICML 2004, p. 69. ACM, Banff (2004)Google Scholar
  11. 11.
    Qin, Z., Zhang, C., Zhang, S.: Cost-sensitive Decision Trees with Multiple Cost Scales. In: Webb, G.I., Yu, X. (eds.) AI 2004. LNCS (LNAI), vol. 3339, pp. 380–390. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Wang, T., Qin, Z., Zhang, S.: Cost-sensitive Learning - A Survey. Accepted by International Journal of Data Warehousing and Mining (2010)Google Scholar
  13. 13.
    Chai, X., Deng, L., Yang, Q., Ling, C.X.: Test-Cost Sensitive Naive Bayes Classification. In: ICDM 2004, pp. 51–58. IEEE Computer Society Press, Brighton (2004)Google Scholar
  14. 14.
    Zhu, X., Wu, X.: Cost-Constrained Data Acquisition for Intelligent Data Preparation. IEEE Transactions on Knowledge and Data Engineering 17(11), 1542–1556 (2005)CrossRefGoogle Scholar
  15. 15.
    Sheng, S., Ling, C.X., Yang, Q.: Simple Test Strategies for Cost-Sensitive Decision Trees. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 365–376. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  16. 16.
    Sheng, V.S., Ling, C.X., Ni, A., Zhang, S.: Cost-Sensitive Test Strategies. In: Proceedings of 21st National Conference on Artificial Intelligence (AAAI 2006), pp. 482–487. AAAI Press, Boston (2006)Google Scholar
  17. 17.
    Zhang, S., Qin, Z., Ling, C.X., Sheng, S.: Missing Is Useful: Missing Values in Cost-Sensitive Decision Trees. IEEE Transactions on Knowledge and Data Engineering 17(12), 1689–1693 (2005)CrossRefGoogle Scholar
  18. 18.
    Qin, Z., Zhang, S., Liu, L., Wang, T.: Cost-sensitive Semi-supervised Classification using CS-EM. In: IEEE 8th International Conference on Computer and Information Technology, pp. 131–136. IEEE Computer Society Press, Sydney (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Zhenxing Qin
    • 1
  • Chengqi Zhang
    • 1
  • Tao Wang
    • 1
  • Shichao Zhang
    • 1
  1. 1.Faculty of Information TechnologyUniversity of Technology, SydneySydneyAustralia

Personalised recommendations