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Communication Dans Un Congrès Année : 2018

CEC-Model: A new competence model for CBR systems based on the belief function theory

Résumé

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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Dates et versions

hal-03643826 , version 1 (16-04-2022)

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Citer

Safa Ben Ayed, Zied Elouedi, Eric Lefevre. CEC-Model: A new competence model for CBR systems based on the belief function theory. International Conference on Case-Based Reasoning, ICCBR'2018, Jul 2018, Stockholm, Sweden. pp.28-44, ⟨10.1007/978-3-030-01081-2_3⟩. ⟨hal-03643826⟩

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