A Knowledge-Light Approach to Regression Using Case-Based Reasoning

  • Neil McDonnell
  • Pádraig Cunningham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4106)


Most CBR systems in operation today are ‘retrieval-only’ in that they do not adapt the solutions of retrieved cases. Adaptation is, in general, a difficult problem that often requires the acquisition and maintenance of a large body of explicit domain knowledge. For certain machine-learning tasks, however, adaptation can be performed successfully using only knowledge contained within the case base itself. One such task is regression (i.e. predicting the value of a numeric variable). This paper presents a knowledge-light regression algorithm in which the knowledge required to solve a query is generated from the differences between pairs of stored cases. Experiments show that this technique performs well relative to standard algorithms on a range of datasets.


Problem Domain Domain Space Nominal Attribute Local Linear Regression Difference Case 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Communications 7(1), 39–59 (1994)Google Scholar
  2. 2.
    Leake, D., Kinley, A., Wilson, D.: Learning to Improve Case Adaptation by Introspective Reasoning and CBR. In: Aamodt, A., Veloso, M.M. (eds.) ICCBR 1995. LNCS, vol. 1010, pp. 229–240. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  3. 3.
    Wilke, W., Vollrath, I., Althoff, K.-D., Bergmann, R.: A framework for learning adaptation knowledge based on knowledge light approaches. In: Proc 5th German Workshop on Case-Based Reasoning (1997)Google Scholar
  4. 4.
    McDonnell, N., Cunningham, P.: Using Case Differences for Regression in CBR Systems. In: Proc 25th Annual International Conference of the BCS SGAI, pp. 219–232. Springer, Heidelberg (2005)Google Scholar
  5. 5.
    McSherry, D.: An adaptation heuristic for case-based estimation. In: Proc 4th European Workshop on Case-Based Reasoning, pp. 184–195. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  6. 6.
    Richter, M.: Introduction. In: Wess, S., Lenz, M., Bartsch-Spörl, B., Burkhard, H.D. (eds.) Case-Based Reasoning Technology. LNCS (LNAI), vol. 1400. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  7. 7.
    Hanney, K., Keane, M.: Learning Adaptation Rules from a Case-Base. In: Smith, I., Faltings, B.V. (eds.) EWCBR 1996. LNCS, vol. 1168, pp. 179–192. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  8. 8.
    Jarmulak, J., Craw, S., Rowe, R.: Using Case-Base Data to Learn Adaptation Knowledge for Design. In: Proc 17th International Conference on Artificial Intelligence, pp. 1011–1020. Morgan Kaufmann, San Francisco (2001)Google Scholar
  9. 9.
    Craw, S.: Introspective Learning to Build Case-Based Reasoning (CBR) Knowledge Containers. In: Perner, P., Rosenfeld, A. (eds.) MLDM 2003. LNCS, vol. 2734, pp. 1–6. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Atkeson, C., Moore, A., Schaal, S.: Locally weighted learning. AI Review (1996)Google Scholar
  11. 11.
    Smyth, B., Keane, M.T.: Adaptation-guided retrieval: Questioning the similarity assumption. Artificial Intelligence 102, 249–293 (1998)MATHCrossRefGoogle Scholar
  12. 12.
    Hettich, S., Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases. University of California, Irvine, CA (1998)Google Scholar
  13. 13.
    Tecator dataset originally compiled by Hans Henrik Thodberg, Danish Meat Research Institute, Maglegaardsvej 2, Postboks 57, DK-4000 Roskilde, Denmark. Available from StatLib: http://lib.stat.cmu.edu/datasets/tecator
  14. 14.
    Witten, I.H., Eibe, F.: Data Mining: Practical machine learning tools with Java implementations, p. 246. Morgan Kaufmann, San Francisco (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Neil McDonnell
    • 1
  • Pádraig Cunningham
    • 1
  1. 1.Department of Computer ScienceTrinity College DublinIreland

Personalised recommendations