Abstract
Case-Based Reasoning (CBR) systems solve new problems using others which have been previously resolved. The knowledge is composed of a set of cases stored in a case memory, where each one describes a situation in terms of a set of features. Therefore, the size and organization of the case memory influences in the computational time needed to solve new situations. We organize the memory using Self-Organization Maps, which group cases with similar properties into patterns. Thus, CBR is able to do a selective retrieval using only the cases from the most suitable pattern. However, the data complexity may hinder the identification of patterns and it may degrade the accuracy rate. This work analyses the successful application of this approach by doing a previous data complexity characterization. Relationships between the performance and some measures of class separability and the discriminative power of attributes are also found.
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
Aamodt, A., Plaza, E.: Case-based reasoning: Foundations issues, methodological variations, and system approaches. IA Communications 7, 39–59 (1994)
Wess, S., Althoff, K.D., Derwand, G.: Using k-d Trees to Improve the Retrieval Step in Case-Based Reasoning. In: Wess, S., Richter, M., Althoff, K.-D. (eds.) EWCBR 1993. LNCS, vol. 837, pp. 167–181. Springer, Heidelberg (1994)
Lenz, M., Burkhard, H.D., Brückner, S.: Applying Case Retrieval Nets to Diagnostic Tasks in Technical Domains. In: Smith, I., Faltings, B.V. (eds.) EWCBR 1996. LNCS, vol. 1168, pp. 219–233. Springer, Heidelberg (1996)
Vernet, D., Golobardes, E.: An Unsupervised Learning Approach for Case-Based Classifier Systems. Expert Update. The Specialist Group on Artificial Intelligence 6(2), 37–42 (2003)
Fornells, A., Golobardes, E., Vernet, D., Corral, G.: Unsupervised case memory organization: Analysing computational time and soft computing capabilities. In: Roth-Berghofer, T.R., Göker, M.H., Güvenir, H.A. (eds.) ECCBR 2006. LNCS (LNAI), vol. 4106, pp. 241–255. Springer, Heidelberg (2006)
Kohonen, T.: Self-Organization and Associative Memory. Series in Information Sciences, vol. 8. Springer, Heidelberg (1989)
Oja, M., Kaski, S., Kohonen, T.: Bibliography of Self-Organizing Map (SOM) Papers: 1998-2001. Neural Computing Surveys 3, 1–156 (2003), http://www.cis.hut.fi/research/refs/
Kaski, S., Kangas, J., Kohonen, T.: Bibliography of Self-Organizing Map (SOM) Papers: 1981-1997. Neural Computing Surveys 1, 102–350 (1998), http://www.cis.hut.fi/research/refs/
Fornells, A., Golobardes, E., VilasÃs, X., MartÃ, J.: Integration of strategies based on relevance feedback into a tool for the retrieval of mammographic images. In: Corchado, E.S., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol. 4224, pp. 116–124. Springer, Heidelberg (2006)
Basu, M., Ho, T.K.: Data Complexity in Pattern Recognition. Springer, Heidelberg (2006)
Ho, T.K., Basu, M.: Complexity measures of supervised classification problems. IEEE Transaction on Pattern Analysis and Machine Intelligence 3(24), 289–300 (2002)
Bernadó-Mansilla, E., Ho, T.K.: Domain of competence of XCS classifier system in complexity measurement space. IEEE Transaction Evolutionary Computation 1(9), 82–104 (2005)
Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998)
Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures. CRC Press, Boca Raton (1997)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Fornells, A., Golobardes, E., Martorell, J.M., Garrell, J.M., Bernadó, E., Maciá, N. (2007). Measuring the Applicability of Self-organization Maps in a Case-Based Reasoning System. In: MartÃ, J., BenedÃ, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72849-8_67
Download citation
DOI: https://doi.org/10.1007/978-3-540-72849-8_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72848-1
Online ISBN: 978-3-540-72849-8
eBook Packages: Computer ScienceComputer Science (R0)