Defining adequate similarity measures is one of the most difficult tasks when developing CBR applications. Unfortunately, only a limited number of techniques for supporting this task by using machine learning techniques have been developed up to now. In this paper, a new framework for learning similarity measures is presented. The main advantage of this approach is its generality, because its application is not restricted to classification tasks in contrast to other already known algorithms. A first refinement of the introduced framework for learning feature weights is described and finally some preliminary experimental results are presented.
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
D. Aha. Case-based learning algorithms. In Proceedings of the DARPA Case-Based Reasoning Workshop, pages 147–158. Morgan Kaufmann, 1991.
D. W. Aha and D Wettschereck. Case-based learning: Beyond classification of feature vectors. In Proceedings of the 9th European Conference on Machine Learning (ECML‘97). Springer, 1997.
R. Bergmann, M. Michael Richter, S. Schmitt, A. Stahl, and I. Vollrath. Utility-oriented matching: A new research direction for Case-Based Reasoning. In Professionelles Wissensmanagement: Erfahrungen und Visionen. Proceedings of the 1st Conference on Professional Knowledge Management. Shaker, 2001.
A. Bonzano, P. Cunningham, and B. Smyth. Using introspective learning to improve retrieval in CBR: A case study in air traffic control. In Proceedings of the 2nd International Conference on Case-Based Reasoning (ICCBR-97). Springer, 1997.
Igor Kononenko. Estimating attributes: Analysis and extensions of RELIEF. In Proceedings of the European Conference on Machine Learning, pages 171–182, 1994.
F. Ricci and P. Avesani. Learning a local similarity metric for case-based reasoning. In Proceeding of the 1st International Conference on Case-Based Reasoning (ICCBR‘95), pages 301–312. Springer, 1995.
Michael M. Richter. Classification and learning of similarity measures. Technical Report SR-92-18, 1992.
Michael M. Richter. The knowledge contained in similarity measures. Invited Talk at ICCBR-95, 1995.
B. Smyth and M. T. Keane. Retrieving adaptable cases: The role of adaptation knowledge in case retrieval. In Proceedings of the 1st European Workshop on Case-Based Reasoning. Springer, 1993.
A. Stahl and R. Bergmann. Applying recursive CBR for the customization of structured products in an electronic shop. In Proceedings of the 5th European Workshop on Case-Based Reasoning. Springer, 2000.
M. Stolpmann and S. Wess. Optimierung der Kundenbeziehung mit CBR-Systemen. Business and Computing. Addison-Wesley, 1999.
Dietrich Wettschereck and David W. Aha. Weighting features. In Proceeding of the 1st International Conference on Case-Based Reasoning (ICCBR‘95), pages 347–358. Springer Verlag, 1995.
W. Wilke and R. Bergmann. Considering decision cost during learning of feature weights. In Proceedings of the 3rd European Workshop on Case-Based Reasoning. Springer, 1996.
Z. Zhang and Q. Yang. Dynamic refiniement of feature weights using quantitative introspective learning. In Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI 99), 1999.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Stahl, A. (2001). Learning Feature Weights from Case Order Feedback. In: Aha, D.W., Watson, I. (eds) Case-Based Reasoning Research and Development. ICCBR 2001. Lecture Notes in Computer Science(), vol 2080. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44593-5_35
Download citation
DOI: https://doi.org/10.1007/3-540-44593-5_35
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42358-4
Online ISBN: 978-3-540-44593-7
eBook Packages: Springer Book Archive