A guide to expert systems
Introduction to knowledge systems
Introduction to knowledge systems
Principles of data mining
Decision Support and Expert Systems: Management Support Systems
Decision Support and Expert Systems: Management Support Systems
Business Expert Systems
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The Role of Domain Knowledge in a Large Scale Data Mining Project
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
Introduction to the special issue on the fusion of domain knowledge with data for decision support
The Journal of Machine Learning Research
Fusion of domain knowledge with data for structural learning in object oriented domains
The Journal of Machine Learning Research
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
Metadata and its impact on libraries: Book Reviews
Journal of the American Society for Information Science and Technology
Evaluating and Tuning Predictive Data Mining Models Using Receiver Operating Characteristic Curves
Journal of Management Information Systems
Instance weighting versus threshold adjusting for cost-sensitive classification
Knowledge and Information Systems
Incorporating domain knowledge into data mining classifiers: An application in indirect lending
Decision Support Systems
Expert Systems with Applications: An International Journal
Tuning Data Mining Methods for Cost-Sensitive Regression: A Study in Loan Charge-Off Forecasting
Journal of Management Information Systems
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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There is currently a growing body of research examining the effects of the fusion of domain knowledge and data mining. This paper examines the impact of such fusion in a novel way by applying validation techniques and training data to enhance the performance of knowledge-based expert systems. We present an algorithm for tuning an expert system to minimize the expected misclassification cost. The algorithm employs data reserved for training data mining models to determine the decision cutoff of the expert system, in terms of the certainty factor of a prediction, for optimal performance. We evaluate the proposed algorithm and find that tuning the expert systemresults in significantly lower costs. Our approach could be extended to enhance the performance of any intelligent or knowledge system that makes cost-sensitive business decisions.