Abstract
Case retrieval from a clustered case memory consists in finding out the clusters most similar to the new input case, and then retrieving the cases from them. Although the computational time is improved, the accuracy rate may be degraded if the clusters are not representative enough due to data geometry. This paper proposes a methodology for allowing the expert to analyze the case retrieval strategies from a clustered case memory according to the required computational time improvement and the maximum accuracy reduction accepted. The mechanisms used to assess the data geometry are the complexity measures. This methodology is successfully tested on a case memory organized by a Self-Organization Map.
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References
Aamodt, A., Plaza, E.: Case-based reasoning: Foundations issues, methodological variations, and system approaches. AI Communications 7, 39–59 (1994)
Basu, M., Ho, T.K.: Data Complexity in Pattern Recognition. In: Advanced Information and Knowledge Processing, Springer, Heidelberg (2006)
Bernadó, E., Ho, T.K.: Domain of competence of XCS classifier system in complexity measurement space. IEEE Transaction Evolutionary Computation 9(1), 82–104 (2005)
Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998)
Brown, M.: A Memory Model for Case Retrieval by Activation Passing. PhD thesis, University of Manchester (1994)
Chang, P., Lai, C.: A hybrid system combining self-organizing maps with case-based reasoning in wholesaler’s new-release book forecasting. Expert Syst. Appl. 29(1), 183–192 (2005)
Demsar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)
Fornells, A., Golobardes, E., Martorell, J.M., Garrell, J.M., Bernadó, E., Macià, N.: Measuring the applicability of self-organization maps in a case-based reasoning system. In: 3rd Iberian Conference on Pattern Recognition and Image Analysis. LNCS, vol. 4478, pp. 532–539. Springer, Heidelberg (2007)
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)
Fornells, A., Golobardes, E., Vilasís, X., Martí, J.: Integration of strategies based on relevance feedback into a tool for retrieval of mammographic images. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol. 4224, pp. 116–124. Springer, Heidelberg (2006) (Selected to be published in the International Journal of Neural Systems)
Ho, T.K., Basu, M.: Complexity measures of supervised classification problems. IEEE Transaction on Pattern Analysis and Machine Intelligence 24(3), 289–300 (2002)
Kohonen, T.: Self-Organization and Associative Memory, 3rd edn. Springer Series in Information Sciences, vol. 8. Springer, Heidelberg (1984)
Lenz, M., Burkhard, H.D., Brückner, S.: Applying case retrieval nets to diagnostic tasks in technical domains. In: Smith, I., Faltings, B.V. (eds.) Advances in Case-Based Reasoning. LNCS, vol. 1168, pp. 219–233. Springer, Heidelberg (1996)
Myllymaki, P., Tirri, H.: Massively parallel case-based reasoning with probabilistic similarity metrics (1993)
Nicholson, R., Bridge, D., Wilson, N.: Decision diagrams: Fast and flexible support for case retrieval and recommendation. In: Roth-Berghofer, T.R., Göker, M.H., Güvenir, H.A. (eds.) ECCBR 2006. LNCS (LNAI), vol. 4106, pp. 136–150. Springer, Heidelberg (2006)
Pelleg, D., Moore, A.: X-means: Extending K-means with efficient estimation of the number of clusters. In: Proceedings of the 17th International Conference of Machine Learning, pp. 727–734. Morgan Kaufmann, San Francisco (2000)
Plaza, E., McGinty, L.: Distributed case-based reasoning. The Knowledge engineering review 20(3), 261–265 (2006)
Rissland, E.L., Skalak, D.B., Friedman, M.: Case retrieval through multiple indexing and heuristic search. In: International Joint Conferences on Artificial Intelligence, pp. 902–908 (1993)
Schaaf, J.W.: Fish and Sink - an anytime-algorithm to retrieve adequate cases. In: Aamodt, A., Veloso, M.M. (eds.) Case-Based Reasoning Research and Development. LNCS, vol. 1010, pp. 538–547. Springer, Heidelberg (1995)
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)
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.) Topics in Case-Based Reasoning. LNCS, vol. 837, pp. 167–181. Springer, Heidelberg (1994)
Yang, Q., Wu, J.: Enhancing the effectiveness of interactive cas-based reasoning with clustering and decision forests. Applied Intelligence 14(1) (2001)
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Fornells, A., Golobardes, E., Martorell, J.M., Garrell, J.M., Macià, N., Bernadó, E. (2007). A Methodology for Analyzing Case Retrieval from a Clustered Case Memory. In: Weber, R.O., Richter, M.M. (eds) Case-Based Reasoning Research and Development. ICCBR 2007. Lecture Notes in Computer Science(), vol 4626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74141-1_9
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DOI: https://doi.org/10.1007/978-3-540-74141-1_9
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