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The Case for Case Based Learning

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Case-Based Reasoning Research and Development (ICCBR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11156))

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Abstract

Case-based reasoning (CBR) systems often refer to diverse machine learning functionalities and algorithms to augment their capabilities. In this article we review the concept of case based learning and define it as the use of case based reasoning for machine learning. We present some of its characteristics and situate it in the context of the major machine learning tasks and machine learning approaches. In doing so, we review the particular manner in which case based learning practices declarative learning, for its main knowledge containers, as well as dynamic induction, through similarity assessment. The central role of analogy as a dynamic induction is highlighted as the cornerstone of case based learning that makes it a method of choice in classification and prediction tasks in particular. We propose a larger understanding, beyond instance-based learning, of case based learning as analogical learning that would promote it as a major contributor of the analogizer approach of machine learning.

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Authors and Affiliations

  1. Computer Science Department, SUNY Oswego, Shineman 396A, 7060 New York 104, Oswego, NY, 13126, USA

    Isabelle Bichindaritz

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  1. Isabelle Bichindaritz
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Correspondence to Isabelle Bichindaritz .

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Editors and Affiliations

  1. Wright State University, Dayton, OH, USA

    Michael T. Cox

  2. Mälardalen University, Västeras, Sweden

    Peter Funk

  3. Mälardalen University, Västeras, Sweden

    Shahina Begum

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Bichindaritz, I. (2018). The Case for Case Based Learning. In: Cox, M., Funk, P., Begum, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2018. Lecture Notes in Computer Science(), vol 11156. Springer, Cham. https://doi.org/10.1007/978-3-030-01081-2_4

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  • DOI: https://doi.org/10.1007/978-3-030-01081-2_4

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-01081-2

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