Abstract
This paper focuses on the problem of choosing the best match among a set of retrieved cases. The Select step is the subtask of case retrieval that produces the case that suggests the solution for the input case. There are many different ways to accomplish this task and we propose an automatic means for it. Following the original motivation of paralleling the human similarity heuristic we argue that the selection of the best match is performed by humans choosing the solution that best represents the set of candidate solutions retrieved. The solution that best represents a given data set is the “most typical” solution. Therefore, we describe an application in a Case-Based Reasoning system using the Theory of Typicality to calculate the Most Typical Value of a given set to automatically perform the Select task. An example illustrates the application.
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References
Aamodt, A. & Plaza, E. (1994). Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Artificial Intelligence Communications, 7 (1), 39–59.
Bezdek, J.C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York.
Cheeseman, P. and Stutz, J. (1996). Bayesian Classification (AutoClass): Theory and Results, chapter 6, Advances in Knowledge Discovery and Data Mining, AAAI Press.
Fisher, D. (1996). Iterative Optimization and Simplification of Hierarchical Clusterings. Journal of Artificial Intelligence Research, 4, 147–180.
Friedman, M., Schneider, M., & Kandel, A. (1989). The use of weighted fuzzy expected value (WFEV) in fuzzy expert systems, Fuzzy Sets and Systems, 31, 37–45.
Friedman, M, Ming, M., & Kandel, A. (1995). On the Theory of Typicality. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 3, 2, 127–142.
Gath, I. & Geva, B. (1989). Unsupervised Optimal Fuzzy Clustering. IEEE Transactions Pattern Anal. Machine Intelligence, PAMI-11, 7, 773–781.
Hennessy, D. and Hinkle, D.(1992). Applying Case-Based Reasoning to Autoclave Loading. IEEE Expert, 7, 5, 21–26.
Hurley, Neil (1993). A priori Selection of Mesh Densities for Adaptive Finite Element Analysis, using a Case-Based Reasoning Approach. Topics in Case-Based Reasoning, (First European Workshop, EWCBR-93). Wess, Stefan, Althoff, Klaus-Dieter & Richter, Michael (editors) Springer-Verlag, 379–391.
Kandel, A. (1982). Fuzzy Techniques in Pattern Recognition, John Wiley, New York.
Kaufmann A. (1975). Introduction to the Theory of Fuzzy Subsets, vol.1, Academic Press, New York.
Kim, Steven H. & Novick, Mark B.(1993). Using clustering techniques to support case reasoning. International Journal of Computer Applications in Technology, 6, 2, 57–73.
Klir, G. & Yuan, B. (1995). Fuzzy Sets and Fuzzy Logic: theory and applications. Prentice-Hall Inc., Upper Saddle River, NJ.
Kolodner, J. (1989). Judging Which is the Best Case for a Case-Based Reasoner. Proceedings of a Workshop on Case-Based Reasoning, 77–81.
Kolodner, J. (1993). Case-Based Reasoning. Morgan Kaufmann, Los Altos, CA,.
Kopeikina, L., Brandau, R. & Lemmon, A. (1988). Case Based Reasoning for Continuous Control. DARPA Proceedings of a Workshop on Case-Based Reasoning, Clearwater Beach, Florida, May 10–13, 250–259.
Macchion, D. & Vo, D. P. (1993). Use of case-based reasoning technique in building expert systems. Future Generation Computer Systems, 9, 4, 311–319.
Mcintyre, S. H., Achabal, D. D. & Miller, C. M. (1993). Applying Case-Based Reasoning to Forecasting Retail Sales. Journal of Retailing, 69 (4), 372–398.
Nakatani, Y., Tsukiyama, M. & Fukuda, T. (1991). Case organization in a case-based engineering design support system. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 3, 1789–1794.
Pal, N. R. & Bezdek, J. C. (1995). On Cluster Validity for the Fuzzy c-Means Model. IEEE Transactions on Fuzzy Systems, 3, 3, 370–379.
Riesbeck, C. K. & Schank, R.C. (1989). Inside Case-Based Reasoning. Erlbaum, Hillsdale, NJ.
Rousseeuw, P. J. (1995). Discussion: Fuzzy Clustering at the Intersection. Technometrics, 37, 3, 283–286.
Shen, Z.L., Lui, H.C. & Ding, L.Y. (1994). Approximate Case-Based Reasoning on Neural Networks. International Journal of Approximate Reasoning, 10 (1), 75–98.
Shinn, H.S. (1988). Abstractional Analogy: A Model of Analogical Reasoning. DARPA Proceedings of a Workshop on Case-Based Reasoning, Florida, May 10–13. Janet Kolodner (ed.) Morgan Kaufmann Publishers, 370–387.
Slade, S. (1991). Case-based reasoning: a research paradigm. AI Magazine, 12, 42–55.
Stottler, R. H.(1994). CBR for Cost and Sales Prediction. AI Expert, (Aug.), 25–33.
Sugeno M. (1977). Fuzzy Measures and Fuzzy Integrals — A Survey. In Fuzzy Automata and Decision Processes, Gupta Madan M., Saridis, George N. & Gaines, Brian R. (eds.), North-Holland, New York.
Vassilliadis, S., Triantafyllos, G. & Kobrasly, W. (1994). A method for computing the most typical fuzzy expected value, FUZZ-IEEE '94, Proceedings of the Third IEEE International Conference on Fuzzy Systems (Orlando, FL), 2040–2045.
Wang, Z. & Klir, G. (1992). Fuzzy Measure Theory. Plenum Press, New York.
Weber-Lee, R., Barcia, R. & Khator, S. (1995). Case-based reasoning for cash flow forecasting using fuzzy retrieval. Case-Based Reasoning Research And Development: First International Conference; proceedings/ICCBR-95, Sesimbra, Portugal, October 23–26. Manuela Veloso & Agnar Aamodt (ed.) Springer-Verlag, 510–519.
Whalen, T. & Schott, B. (1985). Goal-Directed Approximate Reasoning in a Fuzzy Production System. Approximate Reasoning in Expert Systems. M.M. Gupta, A. Kandel, W. Bandler, J.B. Kiszka (editors) Elsevier Science Publishers B.V.(North-Holland).
Whitaker, Leslie A., Stottler, Richard H., Henke, Andrea, e King, James A. (1990). Case-Based Reasoning: Taming the similarity heuristic. Proceedings of the Human Factors Society, 312–315.
Windham, Michael P. (1983). Geometrical Fuzzy Clustering Algorithms. Fuzzy Sets and Systems, 10, 271–279.
Zadeh, L.A. (1975). The Concept of a Linguistic Variable and it's Application to Approximate Reasoning-I. Information Sciences, 8, 199–249.
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Weber-Lee, R., Barcia, R.M., Martins, A., Pacheco, R.C. (1996). Using typicality theory to select the best match. In: Smith, I., Faltings, B. (eds) Advances in Case-Based Reasoning. EWCBR 1996. Lecture Notes in Computer Science, vol 1168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020629
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DOI: https://doi.org/10.1007/BFb0020629
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