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An Algorithm for Conversational Case-Based Reasoning in Classification Tasks

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

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.

Keywords

  • conversational case-based reasoning
  • classification
  • accuracy
  • efficiency
  • transparency

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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)