Automatic refinement of expert system knowledge bases
Automatic refinement of expert system knowledge bases
Knowledge reuse among diagnostic problem-solving methods in the shell-kit D3
International Journal of Human-Computer Studies
Effective and Efficient Knowledge Base Refinement
Machine Learning
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Organising Knowledge Refinement Operators
EUROVAV '99 Collected papers from the 5th European Symposium on Validation and Verification of Knowledge Based Systems - Theory, Tools and Practice
System Refinement in Practice - Using a Formal Method to Modify Real-Life Knowledge
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Data mining tasks and methods: Subgroup discovery: change analysis
Handbook of data mining and knowledge discovery
Expert-guided subgroup discovery: methodology and application
Journal of Artificial Intelligence Research
Active subgroup mining: a case study in coronary heart disease risk group detection
Artificial Intelligence in Medicine
Rapid knowledge capture using subgroup discovery with incremental refinement
Proceedings of the 4th international conference on Knowledge capture
A case-based approach for characterization and analysis of subgroup patterns
Applied Intelligence
Improved Comprehensibility and Reliability of Explanations via Restricted Halfspace Discretization
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
A methodological view on knowledge-intensive subgroup discovery
EKAW'06 Proceedings of the 15th international conference on Managing Knowledge in a World of Networks
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When knowledge systems are deployed into a real-world application, then the maintenance of the knowledge is a crucial success factor. In the past, some approaches for the automatic refinement of knowledge bases have been proposed. Many only provide limited control during the modification and refinement process, and often assumptions about the correctness of the knowledge base and case base are made. However, such assumptions do not necessarily hold for real-world applications. In this paper, we present a novel interactive approach for the user-guided refinement of knowledge bases. Subgroup mining methods are used to discover local patterns that describe factors potentially causing incorrect behavior of the knowledge system. We provide a case study of the presented approach with a fielded system in the medical domain.