Explanation Oriented Retrieval

  • Dónal Doyle
  • Pádraig Cunningham
  • Derek Bridge
  • Yusof Rahman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3155)


This paper is based on the observation that the nearest neighbour in a case-based prediction system may not be the best case to explain a prediction. This observation is based on the notion of a decision surface (i.e. class boundary) and the idea that cases located between the target case and the decision surface are more convincing as support for explanation. This motivates the idea of explanation utility, a metric that may be different to the similarity metric used for nearest neighbour retrieval. In this paper we present an explanation utility framework and present detailed examples of how it is used in two medical decision-support tasks. These examples show how this notion of explanation utility sometimes select cases other than the nearest neighbour for use in explanation and how these cases are more convincing as explanations.


Decision Boundary Utility Measure Oral Hypoglycaemic Agent Explanation Case Target Case 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Armengol, E., Palaudàries, A., Plaza, E.: Individual Prognosis of Diabetes Longterm Risks: A CBR Approach. Methods of Information in Medicine. Special issue on prognostic models in Medicine 40, 46–51 (2001)Google Scholar
  2. 2.
    Aleven, V., Ashley, K.D.: Automated Generation of Examples for a Tutorial in Case-Based Argumentation. In: Frasson, C., McCalla, G.I., Gauthier, G. (eds.) ITS 1992. LNCS, vol. 608, pp. 575–584. Springer, Heidelberg (1992)Google Scholar
  3. 3.
    Ashley, K.D., McLaren, B.: Reasoning with reasons in case-based comparisons. In: Aamodt, A., Veloso, M.M. (eds.) ICCBR 1995. LNCS, vol. 1010, pp. 133–144. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  4. 4.
    Brüninghaus, S., Ashley, K.D.: Combining Model-Based and Case-Based Reasoning for Predicting the Outcomes of Legal Cases. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol. 2689, pp. 65–79. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Coyle, L., Doyle, D., Cunningham, P.: Representing Similarity for CBR in XML. To appear in 7th European Conference in Case-Based Reasoning (2004)Google Scholar
  6. 6.
    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
  7. 7.
    Doyle, D., Loughrey, J., Nugent, C., Coyle, L., Cunningham, P.: FIONN: A Framework for Developing CBR Systems, to appear in Expert UpdateGoogle Scholar
  8. 8.
    Evans-Romaine, K., Marling, C.: Prescribing Exercise Regimens for Cardiac and Pulmonary Disease Patients with CBR. In: Workshop on CBR in the Health Sciences at 5th International Conference on Case-Based Reasoning (ICCBR 2003), Trondheim, Norway, June 24, pp. 45–62 (2003)Google Scholar
  9. 9.
    Kass, A.M., Leake, D.B.: Case-Based Reasoning Applied to Constructing Explanations. In: Kolodner, J. (ed.) Proceedings of 1988 Workshop on Case-Based Reasoning, pp. 190–208. Morgan Kaufmann, San Mateo (1988)Google Scholar
  10. 10.
    Leake, D.B.: CBR in Context: The Present and Future. In: Leake, D.B. (ed.) Case-Based Reasoning: Experiences, Lessons and Future Directions, pp. 3–30. MIT Press, Cambridge (1996)Google Scholar
  11. 11.
    Lenz, M., Burkhard, H.-D.: Case Retrieval Nets: Basic ideas and extensions. In: Görz, G., Hölldobler, S. (eds.) KI 1996. LNCS, vol. 1137, pp. 227–239. Springer, Heidelberg (1996)Google Scholar
  12. 12.
    McSherry, D.: Similarity and Compromise. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS (LNAI), vol. 2689, pp. 122–130. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    McSherry, D.: Explanation in Case-Based Reasoning: an Evidential Approach. In: Procceedings 8th UK Workshop on Case-Based Reasoning, pp. 47–55 (2003)Google Scholar
  14. 14.
    Osborne, H.R., Bridge, D.G.: A Case Base Similarity Framework. In: Smith, I., Faltings, B.V. (eds.) EWCBR 1996. LNCS (LNAI), vol. 1168, pp. 309–323. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  15. 15.
    Ong, L.S., Shepherd, B., Tong, L.C., Seow-Choen, F., Ho, Y.H., Tang, L.C., Ho, Y.S., Tan, K.: The Colorectal Cancer Recurrence Support (CARES) System. Artificial Intelligence in Medicine 11(3), 175–188 (1997)CrossRefGoogle Scholar
  16. 16.
    Rahman, Y., Knape, T., Gargan, M., Power, G., Hederman, L., Wade, V., Nolan, J.J., Grimson, J.: e-Clinic: An electronic triage system in Diabetes Management through leveraging Information and Communication Technologies. Accepted for MedInfo 2004Google Scholar
  17. 17.
    Stahl, A., Gabel, T.: Using Evolution Programs to Learn Local Similarity Measures. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol. 2689, pp. 537–551. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  18. 18.
    Stahl, A.: Defining similarity measures: Top-down vs. Bottom-up. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 406–420. Springer, Heidelberg (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Dónal Doyle
    • 1
  • Pádraig Cunningham
    • 1
  • Derek Bridge
    • 2
  • Yusof Rahman
    • 1
  1. 1.Computer ScienceTrinity CollegeDublin 2Ireland
  2. 2.Computer ScienceUniversity College CorkCorkIreland

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