Abstract
Our goal is to provide some tools, based on soft computing aggregation methods, useful in the two fundamental steps in case base reasoning, matching the target and the cases and fusing the information provided by the relevant cases. To aid in the first step we introduce a methodology for matching the target and cases which uses a hierarchical representation of the target object. We also introduce a method for fusing the information provided by relevant retrieved cases. This approach is based upon the nearest neighbor principle and uses the induced ordered weighted averaging operator as the basic aggregation operator. A procedure for learning the weights is described.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
J. Kolodner, Case Based Reasoning, Morgan Kaufmann: San Mateo, CA, 1993.
D.B. Leake and E. Plaza, Case-Based Reasoning Research and Development, Springer Verlag: Berlin, 1997.
I.D. Watson, Applying Case-Based Reasoning: Techniques for Enterprise Systems, Morgan Kaufmann: San Mateo, CA, 1997.
S.K. Pal, T.S. Dillon, and D.S. Yeung, Soft Computing in Case Based Reasoning, Springer Verlag: Heidelberg, 2001.
A.N. Steinberg, C.L. Bowman, and F.E. White, “Revisions to the JDL data fusion model,” in Proceedings of SPIE Sensor Fusion: Architecture, Algorithms and Applications II, vol. 3719, 1999, pp. 430–441.
M.R. Endsley and D.J. Garland, Situation Awareness Analysis and Measurement, Lawrence Erlbaum Associates: Mahawah, NJ, 2000.
B.V. Dasarathy, Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques, IEEE Computer Science Press: Los Alamitos, CA, 1990.
J.C. Bezdek, J. Keller, R. Krisnapuram, and N.R. Pal, Fuzzy Models and Algorithms for Pattern Recognition and Image Processing, Kluwer: Boston, 1999.
R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification, Wiley Interscience: New York, 2001.
R.R. Yager, “Using fuzzy methods to model nearest neighbor rules,” IEEE Transactions on Systems, Man and Cybernetics: Part B, vol. 32, pp. 512–525, 2002.
R.R. Yager, “On ordered weighted averaging aggregation operators in multi-criteria decision making,” IEEE Transactions on Systems, Man and Cybernetics, vol. 18, pp. 183–190, 1988.
R.R. Yager and J. Kacprzyk, The Ordered Weighted Averaging Operators: Theory and Applications, Kluwer: Norwell, MA, 1997.
L.A. Zadeh, “A computational approach to fuzzy quantifiers in natural languages,” Computing and Mathematics with Applications, vol. 9, pp. 149–184, 1983.
R.R. Yager, S. Ovchinnikov, R. Tong, and H. Nguyen, Fuzzy Sets and Applications: Selected Papers by L.A. Zadeh, JohnWiley & Sons: New York, 1987.
R.R. Yager, “Quantifier guided aggregation using OWA operators,” International Journal of Intelligent Systems, vol. 11, pp. 49–73, 1996.
R.R. Yager, “On the inclusion of importances in OWA aggregations,” in The Ordered Weighted Averaging Operators: Theory and Applications, edited by R.R. Yager and J. Kacprzyk, Kluwer Academic Publishers: Norwell, MA, 1997, pp. 41–59.
R.R. Yager and D.P. Filev, “Induced ordered weighted averaging operators,” IEEE Transaction on Systems, Man and Cybernetics, vol. 29, pp. 141–150, 1999.
H.B. Mitchell and P.A. Schaefer, “Multiple priorities in an induced ordered weighted averaging operator,” International Journal of Intelligent Systems, vol. 15, pp. 317–328, 2000.
R.R. Yager, “Induced aggregation operators,” Fuzzy Sets and Systems, vol. 137, pp. 59–69, 2003.
D.P. Filev and R.R. Yager, “On the issue of obtaining OWA operator weights,” Fuzzy Sets and Systems, vol. 94, pp. 157–169, 1998.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Yager, R.R. Soft Aggregation Methods in Case Based Reasoning. Applied Intelligence 21, 277–288 (2004). https://doi.org/10.1023/B:APIN.0000043560.57137.20
Issue Date:
DOI: https://doi.org/10.1023/B:APIN.0000043560.57137.20