Abstract
This paper proposes a fuzzy-rough method of maintaining Case- Based Reasoning (CBR) systems. The methodology is mainly based on the idea that a large case library can be transformed to a small case library together with a group of adaptation rules, which take the form of fuzzy rules generated by the rough set technique. In paper [1], we have proposed a methodology for case base maintenance which used a fuzzy decision tree induction to discover the adaptation rules; in this paper, we focus on using a heuristic algorithm, i.e., a fuzzy-rough algorithm [2] in the process of simplifying fuzzy rules. This heuristic, regarded as a new fuzzy learning algorithm, has many significant advantages, such as rapid speed of training and matching, generating a family of fuzzy rules which is approximately simplest. By applying such a fuzzy-rough learning algorithm to the adaptation mining phase, the complexity of case base maintenance is reduced, and the adaptation knowledge is more compact and effective. The effectiveness of the method is demonstrated experimentally using two sets of testing data, and we also compare the maintenance results of using fuzzy ID3, in [1], and the fuzzy-rough approach, as in this paper.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
[1]Shiu C.K., Sun. C. H., Wang X.Z. and Yeung S., “Transferring Case Knowledge to Adaptation Knowledge: An Approach for Case Base Maintenance”, Computational Intelligence, Volume 17, Number 2, 2001.
Wang X.Z., Hong, J.R., “Learning optimization in simplifying fuzzy rules”, Fuzzy Sets and Systems 106, 1999.
Kitano, H. and Shimazu, H. “The experience sharing architecture: A case study in corporate-wide case-based software quality control”, In Case-Based Reasoning: Experiences, Lessons, and Future Directions. Edited by Leake, D. Menlo Park, CA, AAAI Press, pp. 235–268, 1996.
Cheetham, W. and Graf, J., “Case-based reasoning in color matching”, In Proceedings of the Second International Conference on Case-Based Reasoning, ICCBR-97, pp. 1–12, 1997.
Deangdej, J., Lukose, D., Tshui, E., Beinat, P. and Prophet, L., “Dynamically creating indices for two million cases: A real world problem”, In Proceedings of the 3rd European Workshop of Case-Based Reasoning, EWCBR-96, pp. 105–119,1996.
Leake, D.B. and Wilson, D.C. “Categorizing Case-Base Maintenance: Dimensions and Directions”, In Proceedings of the 4th European Workshop of Case-Based Reasoning, EWCBR-98, pp. 196–207,1998
Smyth, B. and Keane, M.T., “Remembering to Forget: A Competence-Preserving Case Deletion Policy for Case-based Reasoning systems”, In Proceedings of the 14th International Joint Conference on Artificial Intelligence, IJCAI-95, pp. 377–382, 1995.
Smyth, B. and Mckenna, E., “Modeling the Competence of Case-bases“, In Proceedings of the 4th European Workshop of Case-Based Reasoning, EWCBR-98, pp. 23–25, 1998.
Anand, S.S., Patterson, D., Hughes, J.G. and Bell D.A., “Discovering Case Knowledge using Data Mining”, In Proceedings of the 2nd Pacific Asia Conference on Knowledge Discovery and Data Mining: Current Issues and New Applications, PAKDD-98, pp. 25–35, 1998.
Richter, M. M., “The Knowledge Contained in Similarity Measures”, Invited Talk at ICCBR-95. http://wwwagr.informatil.uni-kl.de/~lsa/CBR/Richtericcbr95remarks.html.
Richter, M. M., “Chapter one: Introduction”, In Case-Based Reasoning Technology: From Foundations to Applications. Edited by Lena, M., Bartsch-Sporl, B., Burkhard, H.D. and Wess, S., Springer-Verlag, Berlin, Germany, pp. 1–15, 1998.
Zadeh L.A., “Fuzzy Sets“, Information and Control, Vol.8, 1965.
Drwal G., “Rough, and Fuzzy Rough Classification Methods Implemented in RClass System”, ?,?.
Pawlak Z., “Rough Set“, International Journal of Computer and Information Sciences, 1982.
Bezdek, J. C., “Pattern recognition with fuzzy objective function algorithms”, Plenum, NewYork, 1981
Kohonen, T., “Self-Organization and Associate Memory”, Springer, Berlin, 1988.
Yuan. Y, Shaw M.J., “Induction of fuzzy decision trees“, Fuzzy Sets and Systems 69, pp.125–139, 1995.
Nozaki, K., Ishibuchi, H. and Tanaka, H, “A simple but powerful heuristic method for generating fuzzy rules from numerical data,” in Fuzzy Sets and Systems, FSS 86, pp.251–270, 1997.
Shiu C.K., Sun. C. H., Wang X.Z. and Yeung S., “Maintaining Case-Based Reasoning Systems Using Fuzzy Decision Trees”, In Proceedings of the 5rd European Workshop of Case-Based Reasoning, EWCBR-00, pp285–296, 2000.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cao, G., Shiu, S., Wang, X. (2001). A Fuzzy-Rough Approach for Case Base Maintenance. In: Aha, D.W., Watson, I. (eds) Case-Based Reasoning Research and Development. ICCBR 2001. Lecture Notes in Computer Science(), vol 2080. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44593-5_9
Download citation
DOI: https://doi.org/10.1007/3-540-44593-5_9
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42358-4
Online ISBN: 978-3-540-44593-7
eBook Packages: Springer Book Archive