Abstract
This paper presents a new version of an existing hybrid model for the development of knowledge-based systems, where case-based reasoning is used as a problem solver. Numeric predictive attributes are modeled in terms of fuzzy sets to define neurons in an associative Artificial Neural Network (ANN). After the Fuzzy-ANN is trained, its weights and the membership degrees in the training examples are used to automatically generate a local distance function and an attribute weighting scheme. Using this distance function and following the Nearest Neighbor rule, a new hybrid Connectionist Fuzzy Case-Based Reasoning model is defined. Experimental results show that the model proposed allows to develop knowledge-based systems with a higher accuracy than when using the original model. The model takes the advantages of the approaches used, providing a more natural framework to include expert knowledge by using linguistic terms.
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.
Similar content being viewed by others
References
Kolodner, J.: An introduction to case-based reasoning. Artificial Intelligence Review 6, 3–34 (1992)
García, M.M., Bello, P.R.: A model and its different applications to case-based reasoning. Knowledge-based systems 9, 465–473 (1996)
Stanfill, C., Waltz, D.: Toward memory-based reasoning. Comm. of ACM 29, 1213–1228 (1986)
McClelland, D., Rumelhart, E.: Explorations in parallel distributed processing. MIT Press, Cambridge (1989)
Kurgan, L., Krzysztof, C.: CAIM Discretization Algorithm. IEEE Transactions on Knowledge and Data Engineering 16(2) (2004)
Zadeh, L.A.: The concept of a lingüistic variable and Its Application to Approximate Reasoning. Information Sciences 8, 199–249 (1975)
Zadeh, L.A.: From Computing with Numbers to Computing with Words -From Manipulation of Measurements to Manipulation of Perceptions. Intelligent Systems and Soft Computing, 3–40 (2000)
Włodzisław, D.: Similarity-based methods: a general framework for classification, approximation and association. Control and Cybernetics 29(4) (2000)
Aha, D.W.: Feature weighting for lazy learning algorithms. In: Liu, H., Motoda, H. (eds.) Feature Extraction, Construction and Selection: A Data Mining Perspective, Kluwer, Norwell, MA (1998)
Morell, C., Bello, R., Grau, R.: Improving k-NN by Using Fuzzy Similarity Functions. In: Lemaître, C., Reyes, C.A., González, J.A. (eds.) IBERAMIA 2004. LNCS (LNAI), vol. 3315, pp. 708–716. Springer, Heidelberg (2004)
Wettschereck, D., Aha, D.W., Mohri, T.: A Review And Empirical Evaluation Of Feature Weighting Methods For A Class Of Lazy Learning Algorithms. Artificial Intelligence Review 11, 273–314 (1997)
Casillas, O., Cordón, F., Herrera, L.: Interpretability improvements to find the balance interpretability-accuracy in fuzzy modeling: an overview. Interpretability issues in fuzzy modeling, vol. 128, Springer (2003)
García, M., Rodriguez, Y., Bello, R.: Usando conjuntos borrosos para implementar un modelo para sistemas basados en casos interpretativos. In: Monard, M.C., Sichman, J.S. (eds.) SBIA 2000 and IBERAMIA 2000. LNCS (LNAI), vol. 1952, Springer, Heidelberg (2000)
Murphy, P.M., Aha, D.W.: UCI Repository of Machine-Learning Databases, http://www.ics.uci.edu/~mlearn/mlrepository.htm
Wilson, D.R., Martinez, T.R.: Improved Heterogeneous Distance Functions. Journal of Artificial Intelligence Research 6(1), 1–34 (1997)
Mitchell, T.M.: The Need for Biases in Learning Generalizations. In: Shavlik, J.W., Dietterich, T.G. (eds.) Readings in Machine Learning, pp. 184–191. Morgan Kaufmann, San Mateo (1990)
Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
Michie, D., Spiegelhalter, D.J., Taylor, C.C.: Machine Learning. Neural and Statistical Classification (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rodriguez, Y., Garcia, M.M., De Baets, B., Morell, C., Bello, R. (2006). A Connectionist Fuzzy Case-Based Reasoning Model. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_17
Download citation
DOI: https://doi.org/10.1007/11925231_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49026-5
Online ISBN: 978-3-540-49058-6
eBook Packages: Computer ScienceComputer Science (R0)