Skip to main content
SpringerLink
Log in

Towards Cost-Sensitive Learning for Real-World Applications

  • Conference paper
New Frontiers in Applied Data Mining (PAKDD 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7104))

Included in the following conference series:

Abstract

Many research work in cost-sensitive learning focused on binary class problems and assumed that the costs are precise. But real-world applications often have multiple classes and the costs cannot be obtained precisely. It is important to address these issues for cost-sensitive learning to be more useful for real-world applications. This paper gives a short introduction to cost-sensitive learning and then summaries some of our previous work related to the above two issues: (1) The analysis of why traditional Rescaling method fails to solve multi-class problems and our method Rescale new . (2) The problem of learning with cost intervals and our CISVM method. (3) The problem of learning with cost distributions and our CODIS method.

The content of this paper is mainly from the Ph.D dissertation of the first author. This research was supported by Startup Foundation of Southeast University (4009001126) and Open Foundation of National Key Laboratory for Novel Software Technology of China (KFKT2011B01).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Brefeld, U., Geibel, P., Wysotzki, F.: Support Vector Machines with Example Dependent Costs. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) ECML 2003. LNCS (LNAI), vol. 2837, pp. 23–34. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  2. Chawla, N., Japkowicz, N., Zhou, Z.-H. (eds.): Proceedings on PAKDD 2009 Workshop on Data Mining When Classes are Imbalanced and Errors Have Costs (2009)

    Google Scholar 

  3. Dietterich, T., Margineantu, D., Provost, F., Turney, P. (eds.): Proceedings of the ICML 2000 Workshop on Cost-Sensitive Learning (2000)

    Google Scholar 

  4. Domingos, P.: MetaCost: A general method for making classifiers cost-sensitive. In: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data mining, San Diego, California, pp. 155–164 (1999)

    Google Scholar 

  5. Drummond, C., Holte, R.C.: Exploiting the cost of (in)sensitivity of decision tree splitting criteria. In: Proceedings of the 17th International Conference on Machine Learning, pp. 239–246. Morgan Kaufmann, San Francisco (2000)

    Google Scholar 

  6. Drummond, C., Holte, R.C.: Cost curves: An improved method for visualizing classifier performance. Machine Learning 65, 95–130 (2006)

    Article  Google Scholar 

  7. Elkan, C.: The foundations of cost-sensitive learning. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence, Seattle, Washington, pp. 973–978 (2001)

    Google Scholar 

  8. Fan, W., Stolfo, S.J., Zhang, J., Chan, P.K.: AdaCost: Misclassification cost-sensitive boosting. In: Proceedings of the 16th International Conference on Machine Learning, Bled, Slovenia, pp. 97–105 (1999)

    Google Scholar 

  9. Fawcett, T.: ROC graphs: Notes and practical considerations for researchers. Tech. rep., HP Laboratories, Palo Alto, CA (2004)

    Google Scholar 

  10. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  11. Hettich, S., Bay, S.D.: The UCI KDD archive. University of California, Department of Information and Computer Science, Irvine, CA (1999), http://kdd.ics.uci.edu

  12. Kolcz, A.: Local sparsity control for Naive Bayes with extreme misclassification costs. In: Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, Illinois, pp. 128–137 (2005)

    Google Scholar 

  13. Kołcz, A., Chowdhury, A.: Improved Naive Bayes for Extremely Skewed Misclassification. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 561–568. Springer, Heidelberg (2005)

    Google Scholar 

  14. Kukar, M., Kononenko, I.: Cost-sensitive learning with neural networks. In: Proceedings of the 13th European Conference on Artificial Intelligence, pp. 445–449 (1998)

    Google Scholar 

  15. Lee, Y., Lin, Y., Wahba, G.: Multicategory support vector machines, theory, and application to the classification of microarray data and satellite radiance data. Journal of American Statistical Association 99(465), 67–81 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  16. Liu, X.-Y., Zhou, Z.-H.: Learning with cost intervals. In: Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, pp. 403–412 (2010)

    Google Scholar 

  17. Lozano, A.C., Abe, N.: Multi-class cost-sensitive boosting with p-norm loss functions. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, Nevada, pp. 506–514 (2008)

    Google Scholar 

  18. Masnadi-Shirazi, H., Vasconcelos, N.: Asymmetric boosting. In: Proceedings of the 24th International Conference, Corvalis, Oregon, pp. 609–61 (2007)

    Google Scholar 

  19. O’Brien, D.B., Gupta, M.R., Gray, R.M.: Cost-sensitive multi-class classification from probability estimates. In: Proceedings of the 25th International Conference on Machine learning, pp. 712–719 (2008)

    Google Scholar 

  20. Provost, F., Domingos, P.M.: Tree induction for probability-based ranking. Machine Learning 52(3), 199–215 (2003)

    Article  MATH  Google Scholar 

  21. Quinlan, J.R.: C4. 5: Programs for machine learning. Morgan Kaufmann (2003)

    Google Scholar 

  22. Saitta, L., Lavrac, N.: Machine learning - a technological roadmap. Tech. rep. University of Amsterdam, The Netherland (2000)

    Google Scholar 

  23. Sheng, V.S., Ling, C.X.: Roulette Sampling for Cost-Sensitive Learning. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 724–731. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  24. Sun, Y., Wong, A.K.C., Wang, Y.: Parameter Inference of Cost-Sensitive Boosting Algorithms. In: Perner, P., Imiya, A. (eds.) MLDM 2005. LNCS (LNAI), vol. 3587, pp. 21–30. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  25. Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 3rd edn. Elsevier (2006)

    Google Scholar 

  26. Ting, K.M., Zheng, Z.: Boosting Trees for Cost-Sensitive Classifications. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 190–195. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  27. Ting, K.M.: A comparative study of cost-sensitive boosting algorithms. In: Proceedings of the 17th International Conference on Machine Learning, Standord, CA, pp. 983–990 (2000)

    Google Scholar 

  28. Ting, K.M.: An instance-weighting method to induce cost-sensitive trees. IEEE Transactions on Knowledge and Data Engineering 14(3), 659–665 (2002)

    Article  Google Scholar 

  29. Turney, P.D.: Cost -sensitive classification: empirical evaluation of a hybrid genetic sensitive classification. Journal of Artificial Intelligence Research 2, 369–409 (1995)

    Google Scholar 

  30. Viola, P., Jones, M.: Fast and robust classification using asymmetric AdaBoost and a detector cascade. In: Advances in Neural Information Processing Systems, vol. 14, pp. 1311–1318 (2002)

    Google Scholar 

  31. Weiss, G.M., Saar-Tsechansky, M., Zadrozny, B. (eds.): Proceedings of the 1st International Workshop on Utility-Based Data Mining. ACM Press, Chicago (2005)

    Google Scholar 

  32. Weiss, G.M., Saar-Tsechansky, M., Zadrozny, B.: Special issue on utility-based data mining. Data Mining and Knowledge Discovery 17(2) (2008)

    Google Scholar 

  33. Xia, F., Yang, Y., Zhou, L., Li, F., Cai, M., Zeng, D.D.: A closed-form reduction of multi-class cost-sensitive learning to weighted multi-class learning. Pattern Recognition 42(7), 1572–1581 (2009)

    Article  MATH  Google Scholar 

  34. Yang, Q., Wu, X.: 10 challenging problems in data mining research. International Journal of Information Technology and Decision Making 5(4), 597–604 (2006)

    Article  Google Scholar 

  35. Zadrozny, B., Elkan, C.: Learning and making decisions when costs and probabilities are both unknown. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, pp. 204–213 (2001)

    Google Scholar 

  36. Zadrozny, B., Langford, J., Abe, N.: Cost-sensitive learning by cost-proportionate example weighting. In: Proceedings of the 3rd IEEE International Conference on Data Mining, Melbourne, Florida, pp. 435–442 (2003)

    Google Scholar 

  37. Zadrozny, B., Weiss, G.M., Saar-Tsechansky, M. (eds.): Proceedings of the Second International Workshop on Utility-Based Data Mining. ACM Press, Philadelphia (2006)

    Google Scholar 

  38. Zhang, Y., Zhou, Z.-H.: Cost-sensitive face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(10), 1758–1769 (2010)

    Article  Google Scholar 

  39. Zhou, Z.-H., Liu, X.-Y.: On multi-class cost-sensitive learning. In: Proceedings of the 21st National Conference on Artificial Intelligence, pp. 567–572 (2006)

    Google Scholar 

  40. Zhou, Z.H., Liu, X.Y.: Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on Knowledge and Data Engineering 18(1), 63–77 (2006)

    Article  Google Scholar 

  41. Zhou, Z.-H., Liu, X.-Y.: On multi-class cost-sensitive learning. Computational Intelligence 26(3), 232–257 (2010)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Southeast University, China

    Xu-Ying Liu

  2. National Key Laboratory for Novel Software Technology, Nanjing University, China

    Xu-Ying Liu & Zhi-Hua Zhou

Authors
  1. Xu-Ying Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhi-Hua Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Engineering and Information Technology, University of Technology Sydney, Broadway, PO Box 123, NSW 2007, Sydney, Australia

    Longbing Cao

  2. Shenzhen Institute of Advanced Technology (SIAT), Chinese Academy of Sciences, 518055, Shenzhen, China

    Joshua Zhexue Huang  & Jun Luo  & 

  3. The University of Melbourne, VIC 3010, Melbourne, Australia

    James Bailey

  4. The University of Auckland, Auckland, New Zealand

    Yun Sing Koh

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Liu, XY., Zhou, ZH. (2012). Towards Cost-Sensitive Learning for Real-World Applications. In: Cao, L., Huang, J.Z., Bailey, J., Koh, Y.S., Luo, J. (eds) New Frontiers in Applied Data Mining. PAKDD 2011. Lecture Notes in Computer Science(), vol 7104. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28320-8_42

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-28320-8_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28319-2

  • Online ISBN: 978-3-642-28320-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation