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2018 | OriginalPaper | Chapter

The Case for Case Based Learning

Author : Isabelle Bichindaritz

Published in: Case-Based Reasoning Research and Development

Publisher: Springer International Publishing

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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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Title: The Case for Case Based Learning
Author: Isabelle Bichindaritz
Publisher: Springer International Publishing
Book: Case-Based Reasoning Research and Development
Print ISBN: 978-3-030-01080-5

Electronic ISBN: 978-3-030-01081-2

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-01081-2_4

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