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Using Evolution Programs to Learn Local Similarity Measures

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Book cover Case-Based Reasoning Research and Development (ICCBR 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2689))

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Abstract

The definition of similarity measures is one of the most crucial aspects when developing case-based applications. In particular, when employing similarity measures that contain a lot of specific knowledge about the addressed application domain, modelling similarity measures is a complex and time-consuming task. One common element of the similarity representation are local similarity measures used to compute similarities between the values of single attributes. In this paper an approach to learn local similarity measures by employing an evolution program— a special form of a genetic algorithm— is presented. The goal of the approach is to learn similarity measures that sufficiently approximate the utility of cases for given problem situations in order to obtain reasonable retrieval results.

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Author information

Authors and Affiliations

  1. Computer Science Department Artificial Intelligence—Knowledge-Based Systems Group, Kaiserslautern University of Technology, 67653, Kaiserslautern, Germany

    Armin Stahl & Thomas Gabel

Authors
  1. Armin Stahl
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  2. Thomas Gabel
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Editor information

Editors and Affiliations

  1. Learning Research and Development Center, University of Pittsburgh, 3939 O’Hara Street, 15260, Pittsburgh, PA, USA

    Kevin D. Ashley

  2. Department of Computer Science, University College Cork, The Kane Building, College Road, Cork, Ireland

    Derek G. Bridge

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© 2003 Springer-Verlag Berlin Heidelberg

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Stahl, A., Gabel, T. (2003). Using Evolution Programs to Learn Local Similarity Measures. In: Ashley, K.D., Bridge, D.G. (eds) Case-Based Reasoning Research and Development. ICCBR 2003. Lecture Notes in Computer Science(), vol 2689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45006-8_41

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  • DOI: https://doi.org/10.1007/3-540-45006-8_41

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40433-0

  • Online ISBN: 978-3-540-45006-1

  • eBook Packages: Springer Book Archive

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