Abstract
Case-based reasoning is a knowledge processing concept that has shown success in various problem classes. One key challenge in CBR is the construction of a measure that adequately models the similarity between two cases. Typically, a similarity measure consists of a set of feature-specific distance functions coupled with an underlying feature weighting (importance) scheme. While the definition of the distance functions is often straightforward, the estimation of the weighting scheme requires a deep understanding of the domain and the underlying connections.
The paper in hand addresses this problem. It shows how discrimination knowledge, which is coded within an already solved classification problem, can be transformed towards a similarity measure. Moreover, it demonstrates our approach at the problem of diagnosing heart diseases.
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References
Aha, D. (1992): Tolerating noisy, irrelevant, and novel attributes in instancebased learning algorithms. International Journal of Man-Machine Studies.
Aha, D. W. (1991): Case-Based Learning Algorithms. Proceedings of the 1991 DARPA Case-Based Reasoning Workshop, Morgan Kaufmann.
Aha, D. W. and Bankert, R. L. (1994): Feature Selection for Case-Based Classification of Cloud Types: An Empirical Comparison. Proceedings of the AAAI-94 Workshop on Case-Based Reasoning, AAAI Press, Seattle, WA, pp. 106–112.
Aha, D. W. and Goldstone, R. (1992): Concept learning and flexible weighting. Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society.
Aha, D. W., Kibler, D., and Albert, M. K. (1991): Instance-Based Learning Algorithms. Machine Learning, 6, 37–66.
Bonzano, A., Cunningham, P., and Smyth, B. (1997): Using introspective learning to improve retrieval in cbr: A case study in air traffic control. Proceedings of the Second ICCBR Conference.
Dash, M. and Liu, H. (1997): Feature Selection for Classification. Intelligent Data Analysis.
Giraud-Carrier, C. and Martinez, T. (1995): An Efficient Metric for Heterogeneous Inductive Learning Applications in the Attribute-Value Language. Intelligent Systems, pp. 241–250.
Hosmer, D. W. and Lemeshow, S. (1989): Applied Logistic Regression. John Wiley & Sons, New York.
Kira, K. and Rendell, L. (1992): A practical approach to feature selection. Proceedings of the Ninth International Conference on Machine Learning.
Kolodner, J. (1994): Case-Based Reasoning. Morgan Kaufmann.
Michalski, R., Carbonell, J. G., and Mitchell, T. (Eds.) (1983): Machine Learning: An Artificial Intelligence Approach. Vol. 1, Tioga, Palo Alto, California.`
Mitchell, T. M. (1982): Generalization as search. Artificial Intelligence, 18 (2), 203–226.
Myers, R. H. (1986): Classical and Modern Regression with Applications. Duxbury Press, Boston.
Pao, Y.-H. (1989): Adaptive Pattern Recognition and Neural Networks. Addison-Wesley, Reading, MA.
Quinlan, J. R. (1986): Induction of Decision Trees. Machine Learning, 1(1), 81–106.
Richter, M. M. (1995): The Knowledge Contained in Similarity Measures. Some remarks on the invited talk given at ICCBR’95 in Sesimbra, Portugal.
Salzberg, S. L. (1991): A nearest hyperrectangle learning method. Machine Learning.
Sarle, W. S. (1994): Neural Networks and Statistical Models. Proceedings of the Nineteenth Annual SAS Users Group International Conference, SAS Institute Inc., Cary, NC, USA, pp. 1538–1550.
Weisberg, S. (1985): Applied Linear Regression. John Wiley & Sons, New York.
Wess, S. and Globig, C. (1994): Case-Based and Symbolic Classification — A Case Study. In: S. Wess, K.-D. Althoff, and M. M. Richter (Eds.): Topics in Case-Based Reasoning, LNAI 837, Springer.
Wilson, D. R. and Martinez, T. R. (1997): Improved Heterogeneous Distance Functions. Journal of Artificial Intelligence Research.
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Stein, B., Niggemann, O., Husemeyer, U. (2000). Learning Complex Similarity Measures. In: Decker, R., Gaul, W. (eds) Classification and Information Processing at the Turn of the Millennium. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-57280-7_28
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DOI: https://doi.org/10.1007/978-3-642-57280-7_28
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