Abstract
Case-based reasoning is one of the most frequently used tools in data mining. Though it has been proved to be useful in many problems, it is noted to have shortcomings such as feature weighting problems. In previous research, we proposed a hybrid system of case-based reasoning and neural network. In the system, the feature weights are extracted from the trained neural network, and used to improve retrieval accuracy of case-based reasoning. However, this system has worked best in domains in which all features had numeric values. When the feature values are symbolic, nearest neighbor methods typically resort to much simpler metrics, such as counting the features that match. A more sophisticated treatment of the feature space is required in symbolic domains. We propose another hybrid system of case-based reasoning and neural network, which uses value difference metric (VDM) for symbolic features. The proposed system is validated by datasets in symbolic domains.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Wettschereck, D., Aha, D.W.: Weighting features. In: Aamodt, A., Veloso, M.M. (eds.) ICCBR 1995. LNCS, vol. 1010, pp. 347–358. Springer, Heidelberg (1995)
Hastie, T., Tibshirani, R.: Discriminant adaptive nearest neighbor classification. Int. J. IEEE Trans. Pattern Anal. Machine Intell. 18, 607–616 (1996)
Wettschereck, D., Aha, D.W., Mohri, T.: A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. AI Rev. 11, 273–314 (1997)
Shin, C.K., Yun, U.T., Kim, H.K., Park, S.C.: A Hybrid Approach of Neural Network and Memory-Based Learning to Data Mining. Int. J. IEEE Trans. on Neural Networks 11(3), 637–646 (2000)
Stanfill, C., Waltz, D.: Toward memory-based reasoning. Communications of the ACM 29(12), 1213–1228 (1996)
Cost, S., Salzberg, S.: A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features. Machine Learning 10, 57–78 (1993)
Blake, C., Keogh, E., Merz, C.J.: UCI Repository of Machine Learning Databases. In: Univ. California, Dept. Inform. Comput. Sci., Irvine, CA (1999)
Shneiderman, B.: Designing the User Interface: Strategies for Effective Human-Computer Interaction. Addison-Wesley, Reading (1997)
Thrun, S.B., et al.: A Performance Comparison of Different Learning Algorithms, Carnegie Mellon University (1991)
Wilson, D.R., Martinez, T.R.: Improved Heterogeneous Distance Functions. J. Artificial Intelligence Research 6, 1–34 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Im, K.H., Kim, T.H., Park, S.C. (2005). Hybrid System of Case-Based Reasoning and Neural Network for Symbolic Features. In: Tjoa, A.M., Trujillo, J. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2005. Lecture Notes in Computer Science, vol 3589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11546849_26
Download citation
DOI: https://doi.org/10.1007/11546849_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28558-8
Online ISBN: 978-3-540-31732-6
eBook Packages: Computer ScienceComputer Science (R0)