Automatic knowledge base refinement for classification systems
Artificial Intelligence
Case-based reasoning
Machine Learning
IEEE Expert: Intelligent Systems and Their Applications
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IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
A critical review of multi-objective optimization in data mining: a position paper
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Journal of Management Information Systems
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Decision Support Systems
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Advances in Artificial Intelligence
Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned
Expert Systems with Applications: An International Journal
A Combined Forecast Method Integrating Contextual Knowledge
International Journal of Knowledge and Systems Science
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We describe techniques for combining two types of knowledge systems: expert and machine learning. Both the expert system and the learning system represent information by logical decision rules or trees. Unlike the classical views of knowledge-base evaluation or refinement, our view accepts the contents of the knowledge base as completely correct. The knowledge base and the results of its stored cases will provide direction for the discovery of new relationships in the form of newly induced decision rules. An expert system called SEAS was built to discover sales leads for computer products and solutions. The system interviews executives by asking questions, and based on the responses, recommends products that may improve a business' operations. Leveraging this expert system, we record the results of the interviews and the program's recommendations. The very same data stored by the expert system is used to find new predictive rules. Among the potential advantages of this approach are (a) the capability to spot new sales trends and (b) the substitution of less expensive probabilistic rules that use database data instead of interviews.