Performance evaluation of fuzzy clustered case-based reasoning
Malik Jahan Khan, Cynthia M. Khan · Journal of Experimental & Theoretical Artificial Intelligence · 2020
Case-based reasoning (CBR) is a nature-inspired machine learning technique. It solves a new problem using the existing similar problems with their solutions stored in central repository known as case-base. It results in continuous growth of the case-base enhancing the problem solving capability of the system but at the same time compromising the performance. First performance challenge is continuous growth of the case-base. Second performance challenge is to handle the performance bottleneck without compromising the relevance of cases with their neighbourhood to solve new problems. Different approaches have been introduced in literature to address this performance challenge. In this work, a knowledge-base maintenance approach using fuzzy clustering has been presented which takes care of the performance bottleneck and does not compromise the problem solving capability of CBR. Performance of the proposed approach has been evaluated on different case-bases and results have been compared with the conventional CBR approach.