Abstract
We begin by examining the limitations of precedent-based explanations of the predicted outcome in case-based reasoning (CBR) approaches to classification and diagnosis. By failing to distinguish between features that support and oppose the predicted outcome, we argue, such explanations are not only less informative than might be expected, but also potentially misleading. To address this issue, we present an evidential approach to explanation in which a key role is played by techniques for the discovery of features that support or oppose the predicted outcome. Often in assessing the evidence provided by a continuous attribute, the problem is where to “draw the line” between values that support and oppose the predicted outcome. Our approach to the selection of such an evidence threshold is based on the weights of evidence provided by values above and below the threshold. Examples used to illustrate our evidential approach to explanation include a prototype CBR system for predicting whether or not a person is over the legal blood alcohol limit for driving based on attributes such as units of alcohol consumed.
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References
Brüninghaus, S., Ashley, K.D.: Combining Case-Based and Model-Based Reasoning for Predicting the Outcome of Legal Cases. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol. 2689, pp. 65–79. Springer, Heidelberg (2003)
McSherry, D.: Explanation in Case-Based Reasoning: an Evidential Approach. In: Lees, B. (ed.) Proceedings of the 8th UK Workshop on Case-Based Reasoning, pp. 47–55 (2003)
Murdock, J.W., Aha, D.W., Breslow, L.A.: Assessing Elaborated Hypotheses: An Interpretive Case-Based Reasoning Approach. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS (LNAI), vol. 2689, pp. 332–346. Springer, Heidelberg (2003)
McSherry, D.: Interactive Case-Based Reasoning in Sequential Diagnosis. Applied Intelligence 14, 65–76 (2001)
McSherry, D.: Mixed-Initiative Intelligent Systems for Classification and Diagnosis. In: Proceedings of the 14th Irish Conference on Artificial Intelligence and Cognitive Science, pp. 146–151 (2003)
Southwick, R.W.: Explaining Reasoning: an Overview of Explanation in Knowledge- Based Systems. Knowledge Engineering Review 6, 1–19 (1991)
Sørmo, F., Aamodt, A.: Knowledge Communication and CBR. In: González-Calero, P. (ed.) Proceedings of the ECCBR 2002 Workshop on Case-Based Reasoning for Education and Training, pp. 47–59 (2002)
McSherry, D.: Similarity and Compromise. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS (LNAI), vol. 2689, pp. 291–305. Springer, Heidelberg (2003)
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)
Leake, D.B.: CBR in Context: the Present and Future. In: Leake, D.B. (ed.) Case-Based Reasoning: Experiences, Lessons & Future Directions, pp. 3–30. AAAI Press/MIT Press (1996)
Cendrowska, J.: PRISM: an Algorithm for Inducing Modular Rules. International Journal of Man-Machine Studies 27, 349–370 (1987)
Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1, 81–106 (1986)
Evans-Romaine, K., Marling, C.: Prescribing Exercise Regimens for Cardiac and Pulmonary Disease Patients with CBR. In: Bichindaritz, I., Marling, C. (eds.) Proceedings of the ICCBR 2003 Workshop on Case-Based Reasoning in the Health Sciences, pp. 45–52 (2003)
Ong, L.S., Shepherd, B., Tong, L.C., Seow-Cheon, F., Ho, Y.H., Tang, C.L., Ho, Y.S., Tan, K.: The Colorectal Cancer Recurrence Support (CARES) System. Artificial Intelligence in Medicine 11, 175–188 (1997)
McSherry, D.: Dynamic and Static Approaches to Clinical Data Mining. Artificial Intelligence in Medicine 16, 97–115 (1999)
Szolovits, P., Pauker, S.G.: Categorical and Probabilistic Reasoning in Medical Diagnosis. Artificial Intelligence 11, 115–144 (1978)
Spiegelhalter, D.J., Knill-Jones, R.P.: Statistical and Knowledge-Based Approaches to Clinical Decision-Support Systems with an Application in Gastroenterology. Journal of the Royal Statistical Society Series A 147, 35–77 (1984)
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McSherry, D. (2004). Explaining the Pros and Cons of Conclusions in CBR. In: Funk, P., González Calero, P.A. (eds) Advances in Case-Based Reasoning. ECCBR 2004. Lecture Notes in Computer Science(), vol 3155. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28631-8_24
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DOI: https://doi.org/10.1007/978-3-540-28631-8_24
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