A Lazy Learning Approach to Explaining Case-Based Reasoning Solutions

  • David McSherry
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7466)

Abstract

We present an approach to explanation in case-based reasoning (CBR) based on demand-driven (or lazy) discovery of explanation rules for CBR solutions. The explanation rules discovered in our approach resemble the classification rules traditionally targeted by rule learning algorithms, and the learning process is adapted from one such algorithm (PRISM). The explanation rule learned for a CBR solution is required to cover both the target problem and the most similar case, and is used together with the most similar case to explain the solution, thus integrating two approaches to explanation traditionally associated with different reasoning modalities. We also show how the approach can be generalized to enable the discovery of explanation rules for CBR solutions based on k-NN. Evaluation of the approach on a variety of classification tasks demonstrates its ability to provide easily understandable explanations by exploiting the generalizing power of rule learning, while maintaining the benefits of CBR as the problem-solving method.

Keywords

case-based reasoning lazy learning explanation confidence 

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References

  1. 1.
    Cunningham, P., Doyle, D., Loughrey, J.: An Evaluation of the Usefulness of Case-Based Explanation. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS (LNAI), vol. 2689, pp. 122–130. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  2. 2.
    Doyle, D., Cunningham, P., Bridge, D.G., Rahman, Y.: Explanation Oriented Retrieval. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 157–168. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Evans-Romaine, K., Marling, C.: Prescribing Exercise Regimens for Cardiac and Pulmonary Disease Patients with CBR. In: McGinty, L. (ed.) ICCBR 2003 Workshop Proceedings, pp. 45–52. NTNU, Dept. of Computer and Information Science, Trondheim (2003)Google Scholar
  4. 4.
    Leake, D., McSherry, D.: Introduction to the Special Issue on Explanation in Case-Based Reasoning. Artif. Intell. Rev. 24, 103–108 (2005)CrossRefGoogle Scholar
  5. 5.
    Massie, S., Craw, S., Wiratunga, N.: A Visualisation Tool to Explain Case-Base Reasoning Solutions for Tablet Formulation. In: Macintosh, A., Ellis, R., Allen, T. (eds.) AI 2004, pp. 222–234. Springer, London (2005)Google Scholar
  6. 6.
    Maximini, R., Freßmann, A., Schaaf, M.: Explanation Service for Complex CBR Applications. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 302–316. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    McSherry, D.: Conversational Case-Based Reasoning in Medical Decision Making. Artif. Intell. Med. 52, 59–66 (2011)CrossRefGoogle Scholar
  8. 8.
    McSherry, D.: Explaining the Pros and Cons of Conclusions in CBR. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 317–330. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Plaza, E., Armengol, E., Ontañón, S.: The Explanatory Power of Symbolic Similarity in Case-Based Reasoning. Artif. Intell. Rev. 24, 145–161 (2005)MATHCrossRefGoogle Scholar
  10. 10.
    Rissland, E.L.: The Fun Begins with Retrieval: Explanation and CBR. In: Roth-Berghofer, T.R., Göker, M.H., Güvenir, H.A. (eds.) ECCBR 2006. LNCS (LNAI), vol. 4106, pp. 1–8. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Roth-Berghofer, T.R.: Explanations and Case-Based Reasoning: Foundational Issues. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 389–403. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Sørmo, F., Cassens, J., Aamodt, A.: Explanation in Case-Based Reasoning – Perspectives and Goals. Artif. Intell. Rev. 24, 109–143 (2005)CrossRefGoogle Scholar
  13. 13.
    Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley, Boston (2006)Google Scholar
  14. 14.
    Cendrowska, J.: PRISM: an Algorithm for Inducing Modular Rules. Int. J. Man. Mach. Stud. 27, 349–370 (1987)MATHCrossRefGoogle Scholar
  15. 15.
    Bramer, M.A.: Principles of Data Mining. Springer, London (2007)MATHGoogle Scholar
  16. 16.
    Bramer, M.A.: Inducer: A Public Domain Workbench for Data Mining. Int. J. Syst. Sci. 36, 909–919 (2005)MATHCrossRefGoogle Scholar
  17. 17.
    Stahl, F., Bramer, M.A.: Induction of Modular Classification Rules: Using Jmax-Pruning. In: Bramer, M.A., Petridis, M., Hopgood, A. (eds.) AI 2010, pp. 79–92. Springer, London (2010)Google Scholar
  18. 18.
    Frank, A., Asuncion, A.: UCI Machine Learning Repository. University of California, Irvine, School of Information and Computer Sciences (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • David McSherry
    • 1
  1. 1.School of Computing and Information EngineeringUniversity of UlsterColeraineNorthern Ireland

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