Abstract
Knowledge acquisition for a case-based reasoning system from domain experts is a bottleneck in the system development process. With the huge amounts of data that have become available, it would be useful to derive automatically representative cases from available databases rather than acquiring them from domain experts. This paper presents two algorithms using similarity-based rough set theory to derive cases automatically from available databases. The first algorithm SRS1 requires the user to decide the similarity thresholds for the objects in a database, while the second algorithm SRS2 can automatically select proper similarity thresholds. These algorithms require fewer parameters from domain experts than other case generation algorithms. Also they can tackle noise and inconsistent data in the database and select a reasonable number of the representative cases from the database. The experimental results were compared with those from well-known data mining systems, such as rule induction systems and neural network systems.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Aha, D.W., Kibler, D., Albert, M.K., Instance based learning algorithms. Machine Learning 6 37–66, 1991.
Bradshaw, G., Learning about speech sounds: The NEXUS project. Proceedings of the Fourth International Workshop on Machine Learning 1–11 1987
Cao, G., Shiu, S., and Wang, X., A fuzzy-rough approach for case base maintenance. Proceedings of the Forth International Conference on Case-Based Reasoning, 118–130, 2001.
Chan, C., Chen, L., and Geng, L. Knowledge engineering for an intelligent case-based system for help desk operations. Expert System with Application, 18, 125–132, 2000.
Cost, S. and SalzBerg, S. (1990) A weighted nearest neighbor algorithm for learning with symbolic features. Technical Report JHU-90/11. Baltimore, MD: The Johns Hopkins University, Department of Computer Science.
Funakoshi, K. and Bao Ho, T. Rough set approach to information retrieval. Rough Sets in Knowledge Discovery, v2, Lech Polkowski, Andrzej Skowron (eds.). Heidelberg Publisher, New York: Physica-Verlag 166–177, 1998.
Grzymala-Busse, J.W., LERS—a system for learning from examples based on rough sets. In Slowinski, R. (eds.), Intelligent Decision Support, Kluwer Academic Publishers, 3–18, 1992.
Han, J. and Kamber, K. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 2000.
Ketler, K. Case based reasoning: an introduction. Expert System with Application, 6, 3–8, 1993.
Krzysztof Krawiec, Roman Slowinski, and Daniel Vanderpooten, Learining Decision Rules from Similarity Based Rough Approximations 2: Applications, Case Studies and Software Systems. Rough Sets in Knowledge Discovery, v2, Lech Polkowski, Andrzej Skowron (eds.), Heidelberg; New York: Physica-Verlag, 37–54, 1998.
Merz, C.J., Murphy, P.M.,UCI Repository of machine learning databases. University of California, Department of Information and Computer Science, 1996.
Mrozek, A. and Skabek, K. Rough sets in economic applications. Rough Sets in Knowledge Discovery, v2, Lech Polkowski, Andrzej Skowron (eds.). Heidelberg Publisher, New York: Physica-Verlag 238–271, 1998.
Pal, S. K. and Mitra, P. Case generation: a rough-fuzzy approach. Proceedings of Workshop Program at the 4th International Conference on Case-Based Reasoning, 236–242, 2001.
Pawalk, Z. Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Dordrecht, 1991.
Quinlan, J.R. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo CA, 1988.
Rumelhart, D.E., Hinton, G. E., Williams, R.J. Learning internal representations by error propagation. In: Rumelhart, D.E, McClelland, J.L. and the PDP Research Group (eds.), Parallel distributed processing: Explorations in the microstructure of cognition, MIT Press, Cambridge MA, 318–362, 1986.
Slowinski, R and Vanderpoonten D. Similarity relation as a basis for rough approximations. Advances in Machine Intelligence and Soft Computing 4, 17–33, 1997.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Geng, L., Chan, C.W. (2002). Automated Case Generation from Databases Using Similarity-Based Rough Approximation. In: Coello Coello, C.A., de Albornoz, A., Sucar, L.E., Battistutti, O.C. (eds) MICAI 2002: Advances in Artificial Intelligence. MICAI 2002. Lecture Notes in Computer Science(), vol 2313. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46016-0_33
Download citation
DOI: https://doi.org/10.1007/3-540-46016-0_33
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43475-7
Online ISBN: 978-3-540-46016-9
eBook Packages: Springer Book Archive