Skip to main content
SpringerLink
Log in
Book cover

International Conference on Case-Based Reasoning

ICCBR 2010: Case-Based Reasoning. Research and Development pp 201–212Cite as

Detecting Change via Competence Model

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6176))

Abstract

In real world applications, interested concepts are more likely to change rather than remain stable, which is known as concept drift. This situation causes problems on predictions for many learning algorithms including case-base reasoning (CBR). When learning under concept drift, a critical issue is to identify and determine “when” and “how” the concept changes. In this paper, we developed a competence-based empirical distance between case chunks and then proposed a change detection method based on it. As a main contribution of our work, the change detection method provides an approach to measure the distribution change of cases of an infinite domain through finite samples and requires no prior knowledge about the case distribution, which makes it more practical in real world applications. Also, different from many other change detection methods, we not only detect the change of concepts but also quantify and describe this change.

This is a preview of subscription content, 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   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Widmer, G., Kubat, M.: Effective learning in dynamic environments by explicit context tracking. In: Brazdil, P.B. (ed.) ECML 1993. LNCS, vol. 667, pp. 227–243. Springer, Heidelberg (1993)

    Google Scholar 

  2. Widmer, G., Kubat, M.: Learning in the Presence of Concept Drift and Hidden Contexts. Machine Learning 23(1), 69–101 (1996)

    Google Scholar 

  3. Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 97–106. ACM Press, San Francisco (2001)

    Google Scholar 

  4. Cohen, L., Avrahami, G., Last, M., Kandel, A.: Info-fuzzy algorithms for mining dynamic data streams. Applied Soft Computing 8(4), 1283–1294 (2008)

    Article  Google Scholar 

  5. Tsymbal, A.: The Problem of Concept Drift: Definitions and Related Work. Technical Re-port TCD-CS-2004-15, Department of Computer Science, Trinity College Dublin, Ireland (2004)

    Google Scholar 

  6. Tsai, C.-J., Lee, C.-I., Yang, W.-P.: Mining decision rules on data streams in the presence of concept drifts. Expert Syst. Appl. 36(2), 1164–1178 (2009)

    Article  Google Scholar 

  7. Maloof, M.A., Michalski, R.S.: Incremental learning with partial instance memory. Artificial Intelligence 154(1-2), 95–126 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  8. Delany, S.J., Cunningham, P., Tsymbal, A., Coyle, L.: A case-based technique for tracking concept drift in spam filtering. Knowledge-Based Systems 18(4-5), 187–195 (2005)

    Article  Google Scholar 

  9. Klinkenberg, R.: Learning drifting concepts: Example selection vs. example weighting. Intell. Data Anal. 8(3), 281–300 (2004)

    Google Scholar 

  10. Street, W.N., Kim, Y.: A streaming ensemble algorithm (SEA) for large-scale classification. In: 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 377–382. ACM Press, San Francisco (2001)

    Google Scholar 

  11. Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 226–235. ACM Press, Washington (2003)

    Google Scholar 

  12. Kolter, J.Z., Maloof, M.A.: Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts. J. Mach. Learn. Res. 8, 2755–2790 (2007)

    Google Scholar 

  13. Zhang, P., Zhu, X., Shi, Y., Wu, X.: An Aggregate Ensemble for Mining Concept Drifting Data Streams with Noise. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS, vol. 5476, pp. 1021–1029. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  14. Tsymbal, A., Pechenizkiy, M., Cunningham, P., Puuronen, S.: Dynamic integration of classifiers for handling concept drift. Information Fusion 9(1), 56–68 (2008)

    Article  Google Scholar 

  15. Fan, W.: Systematic data selection to mine concept-drifting data streams. In: 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 128–137. ACM Press, Seattle (2004)

    Chapter  Google Scholar 

  16. Kifer, D., Ben-David, S., Gehrke, J.: Detecting change in data streams. In: 13th International Conference on Very Large Data Bases. VLDB Endowment, Toronto, Canada, pp. 180–191 (2004)

    Google Scholar 

  17. Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with Drift Detection. In: 17th Brazilian Symposium on Artificial Intelligence, pp. 286–295. Springer, Sao Luis (2004)

    Google Scholar 

  18. Nishida, K., Yamauchi, K.: Detecting Concept Drift Using Statistical Testing. In: 10th International Conference on Discovery Science, pp. 264–269. Springer, Heidelberg (2007)

    Google Scholar 

  19. Song, X., Wu, M., Jermaine, C., Ranka, S.: Statistical change detection for multi-dimensional data. In: 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 667–676. ACM Press, San Jose (2007)

    Chapter  Google Scholar 

  20. Dries, A., Rückert, U.: Adaptive concept drift detection. Statistical Analysis and Data Mining 2(5-6), 311–327 (2009)

    Article  MathSciNet  Google Scholar 

  21. Massie, S., Craw, S., Wiratunga, N.: What is CBR competence? BCS-SGAI Expert Update 8(1), 7–10 (2005)

    Google Scholar 

  22. Smyth, B., Keane, M.T.: Remembering To Forget: A Competence-Preserving Case Deletion Policy for Case-Based Reasoning Systems. In: 14th International Joint Conference on Arti-ficial Intelligence, pp. 377–382. Morgan Kaufmann, Montreal (1995)

    Google Scholar 

  23. Smyth, B., McKenna, E.: Footprint-Based Retrieval. In: 3rd International Conference on Case-Based Reasoning and Development, pp. 343–357. Springer, Seeon Monastery (1999)

    Chapter  Google Scholar 

  24. Smyth, B., McKenna, E.: Competence Models and the Maintenance Problem. Computational Intelligence 17(2), 235–249 (2001)

    Article  Google Scholar 

  25. Lu, N., Lu, J., Zhang, G.: Maintaining Footprint-Based Retrieval for Case Deletion. In: Leung, C.S., Lee, M., Chan, J.H. (eds.) ICONIP 2009, Part II. LNCS, vol. 5864, pp. 318–325. Springer, Heidelberg (2009)

    Google Scholar 

  26. Gao, J., Fan, W., Han, J.: On Appropriate Assumptions to Mine Data Streams: Analysis and Practice. In: 7th IEEE International Conference on Data Mining, pp. 143–152. IEEE Computer Society, Omaha (2007)

    Google Scholar 

  27. Stanley, K.O.: Learning concept drift with a committee of decision trees. Technical Report UT-AI-TR-03-302, Department of Computer Science, University of Texas at Austin, USA (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Decision Systems & e-Service Intelligence (DeSI) Lab, Centre for Quantum Computation & Intelligent Systems (QCIS), Faculty of Engineering and Information Technology, University of Technology, Sydney, P.O. Box 123, Broadway, NSW, 2007, Australia

    Ning Lu, Guangquan Zhang & Jie Lu

Authors
  1. Ning Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guangquan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jie Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Technology, University of Washington, Tacoma, 1900 Commerce Street, Box 358426, 98402, Tacoma, WA, USA

    Isabelle Bichindaritz

  2. Dipartimento di Informatica, Università del Piemonte Orientale, P.O. Box, Alessandria, Italy

    Stefania Montani

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lu, N., Zhang, G., Lu, J. (2010). Detecting Change via Competence Model. In: Bichindaritz, I., Montani, S. (eds) Case-Based Reasoning. Research and Development. ICCBR 2010. Lecture Notes in Computer Science(), vol 6176. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14274-1_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-14274-1_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14273-4

  • Online ISBN: 978-3-642-14274-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation