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2018 | OriginalPaper | Chapter

CEC-Model: A New Competence Model for CBR Systems Based on the Belief Function Theory

Authors : Safa Ben Ayed, Zied Elouedi, Eric Lefèvre

Published in: Case-Based Reasoning Research and Development

Publisher: Springer International Publishing

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Abstract

The high influence of case bases quality on Case-Based Reasoning success gives birth to an important study on cases competence for problems resolution. The competence of a case base (CB), which presents the range of problems that it can successfully solve, depends on various factors such as the CB size and density. Besides, it is not obvious to specify the exactly relationship between the individual and the overall cases competence. Hence, numerous Competence Models have been proposed to evaluate CBs and predict their actual coverage and competence on problem-solving. However, to the best of our knowledge, all of them are totally neglecting the uncertain aspect of information which is widely presented in cases since they involve real world situations. Therefore, this paper presents a new competence model called CEC-Model (Coverage & Evidential Clustering based Model) which manages uncertainty during both of cases clustering and similarity measurement using a powerful tool called the belief function theory.

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previous chapter Answering with Cases: A CBR Approach to Deep Learning

next chapter The Case for Case Based Learning

Other CBs are offering similar results but are not presented here due to lack of space.

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Title: CEC-Model: A New Competence Model for CBR Systems Based on the Belief Function Theory
Authors: Safa Ben Ayed
Zied Elouedi
Eric Lefèvre
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_3

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