C4.5: programs for machine learning
C4.5: programs for machine learning
Applying case-based reasoning: techniques for enterprise systems
Applying case-based reasoning: techniques for enterprise systems
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Computing with Conceptual Structures
ICCS '00 Proceedings of the Linguistic on Conceptual Structures: Logical Linguistic, and Computational Issues
Conceptual Graph Matching for Semantic Search
ICCS '02 Proceedings of the 10th International Conference on Conceptual Structures: Integration and Interfaces
Using transformations to improve semantic matching
Proceedings of the 2nd international conference on Knowledge capture
TreeRank: a similarity measure for nearest neighbor searching in phylogenetic database
SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
Interactive knowledge validation in CBR for decision support in medicine
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
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In most case-based reasoning (CBR) systems there has been little research done on validating new knowledge, specifically on how previous knowledge differs from current knowledge as a result of conceptual change. This paper proposes two methods that enable the domain expert, who is nonexpert in artificial intelligence (AI), to interactively supervise the knowledge validation process in a CBR system, and to enable dynamic updating of the system, to provide the best diagnostic questions. The first method is based on formal concept analysis which involves a graphical representation and comparison of the concepts, and a summary description highlighting the conceptual differences. We propose a dissimilarity metric for measuring the degree of variation between the previous and current concepts when a new case is added to the knowledge base. The second method involves determining unexpected classification-based association rules to form critical questions as the knowledge base gets updated.