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International Conference on Innovative Techniques and Applications of Artificial Intelligence

SGAI 2011: Research and Development in Intelligent Systems XXVIII pp 167–180Cite as

Choosing a Case Base Maintenance Algorithm using a Meta-Case Base

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Abstract

In Case-Based Reasoning (CBR), case base maintenance algorithms remove noisy or redundant cases from case bases. The best maintenance algorithm to use on a particular case base at a particular stage in a CBR system’s lifetime will vary. In this paper, we propose a meta-case-based classifier for selecting the best maintenance algorithm. The classifier takes in a description of a case base that is to undergo maintenance, and uses meta-cases—descriptions of case bases that have undergone maintenance—to predict the best maintenance algorithm. For describing case bases, we use measures of dataset complexity. We present the results of experiments that show the classifier can come close to selecting the best possible maintenance algorithms.

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Authors and Affiliations

  1. Department of Computer Science, University College Cork, Cork, Ireland

    Lisa Cummins & Derek Bridge

Authors
  1. Lisa Cummins
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  2. Derek Bridge
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Correspondence to Lisa Cummins .

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Editors and Affiliations

  1. University of Portsmouth, Lion Terrace, Portsmouth, PO1 3HE, United Kingdom

    Max Bramer

  2. School of Computing &, Mathematical Sciences, University of Greenwich, Park Row 30, London, SE10 9LS, United Kingdom

    Miltos Petridis

  3. , School of Computing and Informatics, Nottingham Trent University, Burton Street, Nottingham, NG1 4BU, United Kingdom

    Lars Nolle

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Cummins, L., Bridge, D. (2011). Choosing a Case Base Maintenance Algorithm using a Meta-Case Base. In: Bramer, M., Petridis, M., Nolle, L. (eds) Research and Development in Intelligent Systems XXVIII. SGAI 2011. Springer, London. https://doi.org/10.1007/978-1-4471-2318-7_12

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  • DOI: https://doi.org/10.1007/978-1-4471-2318-7_12

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-2317-0

  • Online ISBN: 978-1-4471-2318-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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