Abstract
Case retrieval is an important problem in several commercially significant application areas, such as industrial configuration and manufacturing problems. In this paper we extend the Bayesian probability theory based approaches to case-based reasoning, focusing on the case matching task, an essential part of any case retrieval system. Traditional approaches to the case matching problem typically rely on some distance measure, e.g., the Euclidean or Hamming distance, although there is no a priori guarantee that such measures really reflect the useful similarities and dissimilarities between the cases. One of the main advantages of the Bayesian framework for solving this problem is that it forces one to explicitly recognize all the assumptions made about the problem domain, which helps in analyzing the performance of the resulting system. As an example of an implementation of the Bayesian case matching approach in practice, we demonstrate how to construct a case retrieval system based on a set of independence assumptions between the domain variables. In the experimental part of the paper, the Bayesian case matching metric is evaluated empirically in a case-retrieval task by using public domain discrete real-world databases. The results suggest that case retrieval systems based on the Bayesian case matching score perform much better than case retrieval systems based on the standard Hamming distance similarity metrics.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
D. Aha. A Study of Instance-Based Algorithms for Supervised Learning Tasks: Mathematical, Empirical, an Psychological Observations. PhD thesis, University of California, Irvine, 1990.
D. Aha, editor. Lazy Learning. Kluwer Academic Publishers, Dordrecht, 1997. Reprinted from Artificial Intelligence Review, 11:1–5.
C. Atkeson. Memory based approaches to approximating continuous functions. In M. Casdagli and S. Eubank, editors, Nonlinear Modeling and Forecasting. Proceedings Volume XII in the Santa Fe Institute Studies in the Sciences of Complexity. Addison Wesley, New York, NY, 1992.
C. Atkeson, A. Moore, and S. Schaal. Locally weighted learning. In Aha [2]. pages 11–73.
J.O. Berger. Statistical Decision Theory and Bayesian Analysis. Springer-Verlag, New York, 1985.
G. Cooper and E. Herskovits. A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9:309–347, 1992.
M.H. DeGroot. Optimal statistical decisions. McGraw-Hill, 1970.
B.S. Everitt and D.J. Hand. Finite Mixture Distributions. Chapman and Hall, London, 1981.
D. Fisher. Noise-tolerant conceptual clustering. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 825–830, Detroit, Michigan, 1989.
D. Fisher and D. Talbert. Inference using probabilistic concept trees. In Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, pages 191–202, Ft. Lauderdale, Florida, January 1997.
J.H. Friedman. Flexible metric nearest neighbor classification. Unpublished manuscript. Available by anonymous ftp from Stanford Research Institute (Menlo Park, CA) at playfair.stanford.edu., 1994.
D. Heckerman, D. Geiger, and D.M. Chickering. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20(3):197–243, September 1995.
S. Kasif, S. Salzberg, D. Waltz, J. Rachlin, and D. Aha. Towards a better understanding of memory-based reasoning systems. In Proceedings of the Eleventh International Machine Learning Conference, pages 242–250, New Brunswick, NJ, 1994. Morgan Kaufmann Publishers.
J. Kolodner. Case-Based Reasoning. Morgan Kaufmann Publishers, San Mateo, 1993.
P. Kontkanen, P. Myllymäki, T. Silander, and H. Tirri. A Bayesian approach for retrieving relevant cases. In P. Smith, editor, Artificial Intelligence Applications (Proceedings of the EXPERSYS-97 Conference), pages 67–72, Sunderland, UK, October 1997. IITT International.
P. Kontkanen, P. Myllymäki, T. Silander, and H. Tirri. Bayes optimal instance-based learning. In C. Nédellec and C. Rouveirol, editors, Machine Learning: ECML-98, Proceedings of the 10th European Conference, volume 1398 of Lecture Notes in Artificial Intelligence, pages 77–88. Springer-Verlag, 1998.
P. Kontkanen, P. Myllymäki, T. Silander, H. Tirri, and P. Grünwald. Bayesian and information-theoretic priors for Bayesian network parameters. In C. Nédellec and C. Rouveirol, editors, Machine Learning: ECML-98, Proceedings of the 10th European Conference, Lecture Notes in Artificial Intelligence, Vol. 1398, pages 89–94. Springer-Verlag, 1998.
D. Michie, D.J. Spiegelhalter, and C.C. Taylor, editors. Machine Learning, Neural and Statistical Classification. Ellis Horwood, London, 1994.
A. Moore. Acquisition of dynamic control knowledge for a robotic manipulator. In Seventh International Machine Learning Workshop. Morgan Kaufmann, 1990.
P. Myllymäki and H. Tirri. Massively parallel case-based reasoning with probabilistic similarity metrics. In S. Wess, K.-D. Althoff, and M Richter, editors, Topics in Case-Based Reasoning, volume 837 of Lecture Notes in Artificial Intelligence, pages 144–154. Springer-Verlag, 1994.
C. Stanfill and D. Waltz. Toward memory-based reasoning. Communications of the ACM, 29(12):1213–1228, 1986.
M. Stone. Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society (Series B), 36:111–147, 1974.
H. Tirri, P. Kontkanen, and P. Myllymäki. A Bayesian framework for case-based reasoning. In I. Smith and B. Faltings, editors, Advances in Case-Based Reasoning, volume 1168 of Lecture Notes in Artificial Intelligence, pages 413–427. Springer-Verlag, Berlin Heidelberg, November 1996.
H. Tirri, P. Kontkanen, and P. Myllymäki. Probabilistic instance-based learning. In L. Saitta, editor, Machine Learning: Proceedings of the Thirteenth International Conference, pages 507–515. Morgan Kaufmann Publishers, 1996.
D.M. Titterington, A.F.M. Smith, and U.E. Makov. Statistical Analysis of Finite Mixture Distributions. John Wiley & Sons, New York, 1985.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kontkanen, P., Myllymäki, P., Silander, T., Tirri, H. (1998). On Bayesian case matching. In: Smyth, B., Cunningham, P. (eds) Advances in Case-Based Reasoning. EWCBR 1998. Lecture Notes in Computer Science, vol 1488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056318
Download citation
DOI: https://doi.org/10.1007/BFb0056318
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64990-8
Online ISBN: 978-3-540-49797-4
eBook Packages: Springer Book Archive