A new similarity measure in formal concept analysis for case-based reasoning
Expert Systems with Applications: An International Journal
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Rough set theory and formal concept analysis were invented by Pawlak and Wille in the 1980s and have been applied successfully in several domains. In this paper, we propose a new case-based classifier system based on an integrated rough set theory and formal concept analysis technique. We focus on the construction of a better knowledge base to produce the classification rules. Our system employs rough set theory to discover reduced cases. We then formulate a knowledge base with hierarchical structure by using formal concept analysis. The result is a concept lattice knowledge base embedded in our case-based classifier. We can generate classification rules from implications and subconcept-superconcept relations inside the obtained concept lattice. An illustrative example and a case study are provided to demonstrate the feasibility and applicability of our system. The advantages of our system are thus a better knowledge base for new problem classification and the flexibility to learn new rules.