Abstract
This paper describes how CBR can be used to compare, reuse, and adapt inductive models that represent complex systems. Complex systems are not well understood and therefore require models for their manipulation and understanding. We propose an approach to address the challenges for using CBR in this context, which relate to finding similar inductive models (solutions) to represent similar complex systems (problems). The purpose is to improve the modeling task by considering the quality of different models to represent a system based on the similarity to a system that was successfully modeled. The revised and confirmed suitability of a model can become additional evidence of similarity between two complex systems, resulting in an increased understanding of a domain. This use of CBR supports tasks (e.g., diagnosis, prediction) that inductive or mathematical models alone cannot perform. We validate our approach by modeling software systems, and illustrate its potential significance for biological systems.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Alberts, B., Johnson, A., Lewis, J., Raff, M., Roberts, K., Walter, P.: Molecular Biology of the Cell, 4th edn. Garland Publishing, New York (2002)
Ames, B.N.: DNA Damage from Micronutrient Deficiencies is Likely to Be a Major Cause of Cancer. Mutat Res. 475(1-2), 7–20 (2001)
Armengol, E., Plaza, E.: Relational Case-based Reasoning for Carcinogenic Activity Prediction. Artificial Intelligence Review 20(1-2), 121–141 (2003)
Barr, T.: Architectural Overview of the Computational Intelligence Testing Tool. In: Proceedings of the Eighth IEEE International Symposium on High Assurance Systems Engineering, pp. 269–270. IEEE Computer Society, Los Alamitos (2004)
Bichindaritz, I.: Memoire: Case Based Reasoning Meets the Semantic Web in Biology and Medicine. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 47–61. Springer, Heidelberg (2004)
Bogaerts, S., Leake, D.B.: Facilitating CBR for Incompletely-Described Cases: Distance Metrics for Partial Problem Descriptions. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 62–76. Springer, Heidelberg (2004)
Chakravati, A., Little, P.: Nature, nurture and human disease. Nature 421, 412–414 (2003)
Epel, E.S., Blackburn, E.H., Lin, J., Dhabhar, F.S., Adler, N.E., Morrow, J.D., Cawthon, R.M.: Accelerated Telomere Shortening in Response to Life Stress. Proc. Natl. Acad. Sci. 101(49), 17312–17315 (2004)
Kaput, J., Rodriguez, R.L.: Nutritional Genomics: the Next Frontier in the Postgenomic Era. Physiol. Genomics 16, 166–177 (2004)
Kriete, A., Boyce, K.: Automated tissue analysis – a bioinformatics perspective. Methods Inf. Medicine 1, 32–37 (2005)
Last, M., Friedman, M., Kandel, A.: The Data Mining Approach to Automated Software Testing. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 388–396. ACM Press, New York (2003)
Lu, T., Pan, Y., Kao, S.Y., Li, C., Kohane, I., Chan, J., Yankner, B.A.: Gene Regulation and DNA Damage in the Ageing Human Brain. Nature 429(6994), 883–891 (2004)
Malek, M.: Hybrid Approaches for Integrating Neural Networks and Case-Based Reasoning: From Loosely Coupled to Tightly Coupled Models. In: Pal, S.K., Dillon, T.S., Yeung, D.S. (eds.) Soft Computing in Case Based Reasoning, pp. 73–94. Springer, London (2001)
McFarlane, A.C., Yehuda, R., Clark, C.R.: Biologic Models of Traumatic Memories and Post-Traumatic Stress Disorder. The role of neural networks. Psychiatr. Clin. North Am. 25(2), 253–270 (2002)
Park, E.I., Paisley, E.A., Mangian, H.J., Swartz, D.A., Wu, M., O’Morchoe, P.J., Behr, S.R., Visek, W.J., Kaput, J.: Lipid Level and Type Alter Stearoyl CoA Desaturase mRNA Abundance Differently in Mice with Distinct Susceptibilities to Diet-Influenced Diseases. J. Nutr. 127(4), 566–573 (1997)
Proctor, J.M., Weber, R.: Systematically Evolving Configuration Parameters for Computational Intelligence Methods. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.) PReMI 2005. LNCS, vol. 3776, pp. 376–381. Springer, Heidelberg (2005)
Ren, B., Thelen, A.P., Peters, J.M., Gonzalez, F.J., Jump, D.B.: Polyunsaturated Fatty Acid Suppression of Hepatic Fatty Acid Synthase and S14 Gene Expression Does not Require Peroxisome Proliferator-Activated Receptor-α. J. Biol. Chem. 272, 26827–26832 (1997)
Saraph, P., Last, M., Kandel, A.: Test Set Generation and Reduction with Artificial Neural Networks. In: Last, M., Kandel, A., Bunke, H. (eds.) Artificial Intelligence Methods in Software Testing, pp. 101–132. World Scientific, Singapore (2004)
Shin, C.K., Park, S.C.: Towards Integration of Memory Based Learning and Neural Networks. In: Pal, S.K., Dillon, T.S., Yeung, D.S. (eds.) Soft Computing in Case Based Reasoning, pp. 95–114. Springer, London (2001)
Smyth, B., Keane, M.T.: Experiments on Adaptation-Guided Retrieval in Case-Based Design. In: Aamodt, A., Veloso, M.M. (eds.) ICCBR 1995. LNCS (LNAI), vol. 1010, pp. 313–324. Springer, Heidelberg (1995)
Thomas, R.P., Guigneaux, M., Wood, T., Evers, B.M.: Age-Associated Changes in Gene Expression Patterns in the Liver. J. Gastrointest. Surg. 6(3), 445–453 (2002)
Weber, R., Wu, D.: Knowledge Management for Computational Intelligence Systems. In: Proceedings of the Eighth IEEE International Symposium on High Assurance Systems Engineering, pp. 116–125. IEEE Computer Society, Los Alamitos (2004)
Welle, S., Brooks, A.I., Delehanty, J.M., Needler, N., Thornton, C.A.: Gene Expression Profile of Aging in Human Muscle. Physiol. Genomics 14(2), 149–159 (2003)
Wiemer, J., Schubert, F., Granzow, M., Ragg, T., Fieres, J., Mattes, J., Eils, R.: Informatics United: Exemplary Studies Combining Medical Informatics. Neuroinformatics and Bioinformatics. Methods Inf. Med. 42(2), 126–133 (2003)
Wright, A., Carothers, A.D., Campbell, H.: Gene-environment interactions – the Biobank UK study. Pharmacogenomics J. 2, 75–82 (2002)
Zhao, L.P., Gilbert, S., Defty, C.: E-Diagnosis Using GeneChip Technologies. In: Proceedings of the Fourth International Conference on Advances in Infrastructure for e-Business, e-Education, e-Science, e-Medicine on the Internet. CD-ROM (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Weber, R., Proctor, J.M., Waldstein, I., Kriete, A. (2005). CBR for Modeling Complex Systems. In: Muñoz-Ávila, H., Ricci, F. (eds) Case-Based Reasoning Research and Development. ICCBR 2005. Lecture Notes in Computer Science(), vol 3620. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11536406_47
Download citation
DOI: https://doi.org/10.1007/11536406_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28174-0
Online ISBN: 978-3-540-31855-2
eBook Packages: Computer ScienceComputer Science (R0)