Abstract
One of the perceived benefits of Case-Based Reasoning (CBR) is the potential to use retrieved cases to explain predictions. Surprisingly, this aspect of CBR has not been much researched. There has been some early work on knowledge-intensive approaches to CBR where the cases contain explanation patterns (e.g. SWALE). However, a more knowledge-light approach where the case similarity is the basis for explanation has received little attention. To explore this, we have developed a CBR system for predicting blood-alcohol level. We compare explanations of predictions produced by this system with alternative rule-based explanations. The case-based explanations fare very well in this evaluation and score significantly better than the rule-based alternative.
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References
Armengol, E., Palaudàries, A., Plaza, E., (2001) Individual Prognosis of Diabetes Long-term Risks: A CBR Approach. Methods of Information in Medicine. Special issue on prognostic models in Medicine. vol. 40, pp. 46–51
Bridge, D., Ferguson, A., (2002) An Expressive Query Language for Product Recommender Systems, Artificial Intelligence Review, vol.18, pp.269–307
Clancey, W. J. (1983) The epistemology of a rule-based expert system: A framework for explanation, Artificial Intelligence, 20, 3, 215–251.
Kass, A.M., Leake, D.B., (1988) Case-Based Reasoning Applied to Constructing Explanations, in Proceedings of 1988 Workshop on Case-Based Reasoning, ed. J. Kolodner, pp190–208, Morgan Kaufmann. San Mateo, Ca
Kohavi, R., John, G., (1998) The Wrapper Approach, in Feature Selection for Knowledge Discovery and Data Mining, H. Liu and H. Motoda (eds.), Kluwer Academic Publishers, pp33–50.
Kolodner, J., (1996) Making the Implicit Explicit: Clarifying the Principles of Case-Based Reasoning, in Leake, D.B. (ed) Case-Based Reasoning: Experiences, Lessons and Future Directions, pp349–370, MIT Press
Leake, D., B., (1996) CBR in Context: The Present and Future, in Leake, D.B. (ed) Case-Based Reasoning: Experiences, Lessons and Future Directions, pp3–30, MIT Press
Mark, W., Simoudis, E., Hinkle, D., (1996) Case-Based Reasoning: Expectations and Results, in Leake, D.B. (ed) Case-Based Reasoning: Experiences, Lessons and Future Directions, pp269–294, MIT Press
McSherry, D. (2001) Interactive Case-Based Reasoning in Sequential Diagnosis. Applied Intelligence 14, 65–76
Ong, L.S., Sheperd, B., Tong, L.C., Seow-Choen, F., Ho, Y.H., Tong, L.C., Ho Y.S, Tan, K. (1997) The Colorectal Cancer Recurrence Support (CARES) System. Artificial Intelligence in Medicine 11(3): 175–188.
Quinlan, J.R., (1993) C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, Ca, USA.
Ramberg, R., (1996) Constructing and Testing Explanations in a Complex Domain, in Computers in Human Behaviour, Vol 12, No 1, pp. 29–48.
Riesbeck, C.K., (1988) An Interface for Case-Based Knowledge Acquisition, in Proceedings of 1988 Workshop on Case-Based Reasoning, ed. J. Kolodner, pp312–326, Morgan Kaufmann. San Mateo, Ca.
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Cunningham, P., Doyle, D., Loughrey, J. (2003). An Evaluation of the Usefulness of Case-Based Explanation. In: Ashley, K.D., Bridge, D.G. (eds) Case-Based Reasoning Research and Development. ICCBR 2003. Lecture Notes in Computer Science(), vol 2689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45006-8_12
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DOI: https://doi.org/10.1007/3-540-45006-8_12
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