Abstract
The retrieval-only Case-Based Reasoning (CBR) systems do not provide acceptable accuracy in critical domains such as medical. Besides, the case adaptation process in CBR is often a challenging issue as it has been traditionally carried out manually by domain experts. In this paper, a new case-based approach using transformational adaptation rules called "range adaptation rules" is proposed to improve the accuracy of a retrieval-only CBR system. The rangeadaptation rules are automatically generated from the case-base. In this approach, after solving each new problem, the case-base is expanded and the range adaptation rules are updated automatically. To evaluate the proposed approach, a prototype is implemented and experimented in agriculture domain to classify the IRIS plant types. The experimental results show that the proposed approach increases the classification accuracy comparing with the retrieval-only CBR system.
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.: Case based reasoning. Morgan Kauffman, San Mateo (1993)
Aamodt, A., Plaza, E.: Case-Based Reasoning: Foundational Issues, Methodological Variations and System Approaches. AICOM 7(1), 39–52 (1994)
Baumeister, J., Atzmüller, M., Puppe, F.: Inductive Learning for Case-Based Diagnosis with Multiple Faults. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 28–42. Springer, Heidelberg (2002)
Hunt, J., Miles, R.: Hyprid case-based reasoning. The Knowledge Engineering Review 9(4), 383–397 (1994)
Lenz, M., Bartsch-Spörl, B., Burkhard, H.-D., Wess, S. (eds.): Case-Based Reasoning Technology. LNCS (LNAI), vol. 1400. Springer, Heidelberg (1998)
Macura, R.T., Macura, K.: Case-based reasoning: opportunities and applications in health care. Artificial Intelligence in Medicine 9(1), 1–4 (1997)
Begum, S., Ahmed, M., Funk, P., Xiong, N., Folke, M.: Case-Based Reasoning Systems in the Health Sciences: A Survey of Recent Trends and Developments. IEEE Transactions on Systems, Man, and Cybernetics–Part C: Applications and Reviews 41(4), 421–434 (2011)
Li, H., Li, X., Hu, D., Hao, T., Wenyin, L., Chen, X.: Adaptation rule learning for case based reasoning. Concurrency and Computation: Practice and Experience 21(5), 673–689 (2009)
Yusof, M., Buckingham, C.: Medical case-based reasoning: A review of retrieving, matching and adaptation processes in recent systems. In: Hamza, M.H. (ed.) Proceedings of the Artificial Intelligence and Applications, pp. 72–76. Innsbruck, Austria (2009)
Pal, S.K., Shiu, S.C.K.: Foundations of soft case-based reasoning. Wiley-Interscience, USA (2004)
Chang, C.G., Cui, J.J., Wang, D.W., Hu, K.Y.: Research on case adaptation techniques in case-based reasoning. In: Proc. of ICMLC 2004, pp. 2128–2133 (2004)
Holt, A., Bichindaritz, I., Schmidt, R., Perner, P.: Medical applications in case-based reasoning. The Knowledge Engineering Review 20(3), 286–292 (2006)
Huang, M.J., Chen, M.Y., Lee, S.C.: Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis. Expert Systems with Applications 32(3), 856–867 (2007)
Schmidt, R., Gierl, L.: A prognostic model for temporal courses that combines temporal abstraction and case-based reasoning. International Journal of Medical Informatics 74(2-4), 307–315 (2005)
Schmidt, R., Vorobieva, O.: Case-based reasoning investigation of therapy inefficacy. Knowledge-Based Systems 19(5), 333–340 (2006)
Craw, S., Wiratunga, N., Rowe, R.C.: Learning adaptation knowledge to improve case-based reasoning. Artificial Intelligence 170(16-17), 1175–1192 (2006)
Hanney, K., Keane, M.: Learning Adaptation Rules from a Case-Base. In: Smith, I., Faltings, B.V. (eds.) EWCBR 1996. LNCS, vol. 1168, pp. 179–192. Springer, Heidelberg (1996)
Gu, M., Tong, X., Aamodt, A.: Comparing similarity calculation methods in conversational CBR. In: Proceedings of the 2005 IEEE International Conference on Information Reuse and Integration, Las Vegas, NV, pp. 427–432 (2005)
Machine Learning Repository-Iris Data Set, http://archive.ics.uci.edu/ml/datasets/Iris (accessed July 2012)
Swain, M., Dash, S.K., Dash, S., Mohapatra, A.: An Approach for IRIS Plant Classification Using Neural Network. International Journal on Soft Computing 3(1), 79–89 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sharaf-Eldeen, D.A., Moawad, I.F., El Bahnasy, K., Khalifa, M.E. (2012). Learning and Applying Range Adaptation Rules in Case-Based Reasoning Systems. In: Hassanien, A.E., Salem, AB.M., Ramadan, R., Kim, Th. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2012. Communications in Computer and Information Science, vol 322. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35326-0_48
Download citation
DOI: https://doi.org/10.1007/978-3-642-35326-0_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35325-3
Online ISBN: 978-3-642-35326-0
eBook Packages: Computer ScienceComputer Science (R0)