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SCBM: soft case base maintenance method based on competence model
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DOI：10.1016/j.jocs.2017.09.013.png)
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Abstract
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This paper concerns one of intelligent computational techniques which is case based reasoning (CBR) more particularly the case base maintenance (CBM). It aims to ensure the CBR systems quality. Throughout this paper, we were faced to a problematic question: how to shrink the size of the case base while preserving as much as possible the performance and the competence of the CBR system in soft context. To answer this question, we have first analyzed and revised the theoretical foundations of the existing CBM methods. Then, we have proposed a novel soft case base maintenance (SCBM) method based on a soft competence model (SCM) and a fuzzy clustering technique. Our method has the objective to guarantee the CBR systems efficiency in terms of improving the competence, and reducing both the storage requirements and search time. We support our approach with empirical evaluation using different benchmark data sets to show the effectiveness of our method in terms of improving the competence and the performance of the system. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Machine learning
Case based reasoning
Case base maintenance
Clustering
Competence model
Soft DBSCAN-GM
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