An Algorithm for Conversational Case-Based Reasoning in Classification Tasks

  • David McSherry
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8765)


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.


conversational case-based reasoning classification accuracy efficiency transparency 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • David McSherry
    • 1
  1. 1.School of Computing and Information EngineeringUniversity of UlsterColeraineNorthern Ireland

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