Abstract
This paper presents an extension of an existing hybrid model for the development of knowledge-based systems, combining case-based reasoning (CBR) and artificial neural networks (ANN). The extension consists of the modeling of predictive attributes in terms of fuzzy sets. As such, representative values for numeric attributes are fuzzy sets, facilitating the use of natural language, thus accounting for words with ambiguous meanings. The topology and learning of the associative ANN are based on these representative values. The ANN is used for suggesting the value of the target attribute for a given query. Afterwards, the case-based module justifies the solution provided by the ANN using a similarity function, which includes the weights of the ANN and the membership degrees in the fuzzy sets considered. Experimental results show that the proposed model preserves the advantages of the hybridization used in the original model, while guaranteeing robustness and interpretability.
This work was supported in part by VLIR (Vlaamse InterUniversitaire Raad, Flemish Interuniversity Council, Belgium) under the IUC Program VLIR-UCLV.
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References
Ganascia, J.G.: Explanation and Justification in Expert System. Computers and AI 4(1), 3–13 (1995)
Montani, S., Magni, P., Roudsari, A.V., Carson, E.R., Bellazzi, R.: Integrating different methodologies for insulin therapy support in type 1 diabetic patients. In: Quaglini, S., Barahona, P., Andreassen, S. (eds.) AIME 2001. LNCS (LNAI), vol. 2101, pp. 20–27. Springer, Heidelberg (2001)
Kolodner, J.: An introduction to case-based reasoning. Artificial Intelligence Review 6, 3–34 (1992)
Kass, A.M., et al.: Case-based reasoning applied to constructing explanations. In: Proceeding of workshop on case-based reasoning, Florida (1988)
Kass, A.M., et al.: SWALE: A program that explains. Explanation Patterns Understanding Mechanically and Creatively (1986)
Lopes, A.A., Alipio, J.: Integrating Rules and Cases in Learning via Case Explanation and Paradigm Shift. Springer, Heidelberg (2000)
Fdez-Riverola, F., Corchado, J.M.: Sistemas hÃbridos Neuro-Simbólicos: Una revisión. Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial I(11), 12–26 (2000)
Prentzas, J., Hatzilygeroudis, I.: Integrating Hybrid Rule-Based with Case-Based Reasoning. LNCS (LNAI), pp. 336–349. Springer, Heidelberg (2002)
Hatzilygeroudis, I., et al.: Neurules: Improving the performance of symbolic rules. International Journal on Artificial Intelligence Tools (IJAIT)Â 9(1) (2000)
GarcÃa, 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, Rumelhart, D.E.: Explorations in parallel distributed processing. MIT Press, Cambridge (1989)
Jang, J.-S.R., Sun, C.-T., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice-Hall, Englewood Cliffs (1997)
Pal, S.K., Dillon, T.S., Yeung, D.S.: Soft Computing in Case Based Reasoning. Springer, London (2001)
Zadeh, L.A.: The concept of a linguistic variable and Its Application to Approximate Reasoning. Information Sciences 8, 199–249 (1975)
Casillas, O., Cordón, O., Herrera, F., Magdalena, L.: Interpretability improvements to find the balance interpretability-accuracy in fuzzy modeling: an overview. Interpretability issues in fuzzy modeling, 28, Springer (2003)
Sima, J.: Neural Expert System. Neural Networks 8(2) (1995)
Kurgan, L., et al.: CAIM Discretization Algorithm. IEEE Transactions on Knowledge and Data Engineering 16(2) (2004)
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)
Hong, J., Wang, X.: On the handling of fuzziness for continuous-valued attributes in decision tree generation. Fuzzy Sets and Systems 99(3), 283–290 (1998)
GarcÃa, 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.) Proceedings of IBERAMIASBIA, Sao Paulo, Brasil (2000)
Len, C.-T., Lee, C.S.: Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent System. Prentice-Hall, Englewood Cliffs (1999)
Nauck, D., Klawonn, F., Kruse, R.: Foundations of Neuro-Fuzzy Systems. John Wiley & Sons, Chichester (1997)
De Baets, B., De Meyer, H., Naessens, H.: On rational cardinality-based inclusion measures. Fuzzy Sets and Systems 128, 169–183 (2002)
Zadeh, L.A.: A Computational Approach to Fuzzy Quantifiers in Natural Language. Computing and Mathematics with Applications 9(1), 149–184 (1983)
Murphy, P.M., Aha, D.W.: UCI Repository of Machine-Learning Databases, http://www.ics.uci.edu/~mlearn/mlrepository.htm
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Rodriguez, Y., Garcia, M.M., De Baets, B., Bello, R., Morell, C. (2006). Extending a Hybrid CBR-ANN Model by Modeling Predictive Attributes Using Fuzzy Sets. In: Sichman, J.S., Coelho, H., Rezende, S.O. (eds) Advances in Artificial Intelligence - IBERAMIA-SBIA 2006. IBERAMIA SBIA 2006 2006. Lecture Notes in Computer Science(), vol 4140. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11874850_28
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DOI: https://doi.org/10.1007/11874850_28
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