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Title:
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EXPLOITING PRIOR KNOWLEDGE FOR EVALUATING CBR SYSTEMS USING A COVERAGE & CONSTRAINED
EVIDENTIAL CLUSTERING BASED MODEL |
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Author(s):
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Safa Ben Ayed, Zied Elouedi, Eric Lefevre |
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ISBN:
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978-989-8533-95-1 |
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Editors:
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Hans Weghorn |
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Year:
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2019 |
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Edition:
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Single |
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Keywords:
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Case-Based Reasoning Systems, Evaluation, Competence Model, Prior Knowledge, Machine Learning, Uncertainty |
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Type:
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Full Paper |
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First Page:
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171 |
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Last Page:
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178 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Knowledge resource evaluation presents a concern of widespread interest in intelligent and knowledge management
systems. For instance, the competence of Case Based Reasoning (CBR) systems in solving new problems depends mainly
on the concept of cases coverage of the problem space. In this paper, we propose a new model for Case Base competence
estimation that is based on cases coverage and partitioning, and enables the exploitation of available prior knowledge. This
kind of background, which is handled in form of pairwise constraints, may be offered by domain-experts to aid the
automated learning process. Actually, it is also important to manage the uncertainty involved by CBR systems' knowledge,
since they reflect real-world situations. Our proposal tackles this latter problem under the belief function framework. |
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