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An Algorithm for Conversational Case-Based Reasoning in Classification Tasks

  • Conference paper
Case-Based Reasoning Research and Development (ICCBR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8765))

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Abstract

An important benefit of conversational case-based reasoning (CCBR) in applications such as customer help-desk support is the ability to solve problems by asking a small number of well-selected questions. However, there have been few investigations of the effectiveness of CCBR in classification problem solving, or its ability to compete with k-NN and other machine learning algorithms in terms of accuracy. We present a CCBR algorithm for classification tasks and demonstrate its ability to achieve high levels of problem-solving efficiency, while often equaling or exceeding the accuracy of k-NN and C4.5, a widely used algorithm for decision tree learning.

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Author information

Authors and Affiliations

  1. School of Computing and Information Engineering, University of Ulster, Coleraine, BT52 1SA, Northern Ireland

    David McSherry

Authors
  1. David McSherry
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Software Engineering, Université Laval, G1K 7P4, Québec, Canada

    Luc Lamontagne

  2. IIIA, Artificial Intelligence Research Institute CSIC, Spanish Council for Scientific Research Campus UAB, 08193, Bellaterra, Catalonia, Spain

    Enric Plaza

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McSherry, D. (2014). An Algorithm for Conversational Case-Based Reasoning in Classification Tasks. In: Lamontagne, L., Plaza, E. (eds) Case-Based Reasoning Research and Development. ICCBR 2014. Lecture Notes in Computer Science(), vol 8765. Springer, Cham. https://doi.org/10.1007/978-3-319-11209-1_21

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  • DOI: https://doi.org/10.1007/978-3-319-11209-1_21

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11208-4

  • Online ISBN: 978-3-319-11209-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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