Automatic knowledge base refinement for classification systems
Artificial Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Integration of simulation modeling and inductive learning in an adaptive decision support system
Decision Support Systems - Special issue on model management systems
Learning Boolean concepts in the presence of many irrelevant features
Artificial Intelligence
Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning: Experiences, Lessons and Future Directions
Expert Systems: Design and Development
Expert Systems: Design and Development
Applied Intelligence
Overcoming Process Delays with Decision Tree Induction
IEEE Expert: Intelligent Systems and Their Applications
Machine Learning
Exploiting Inductive Bias Shift in Knowledge Acquisition from Ill-Structured Domains
EKAW '97 Proceedings of the 10th European Workshop on Knowledge Acquisition, Modeling and Management
Introduction to Linear Regression Analysis, Solutions Manual (Wiley Series in Probability and Statistics)
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Fuzzy rule generation for adaptive scheduling in a dynamic manufacturing environment
Applied Soft Computing
Case learning for CBR-based collision avoidance systems
Applied Intelligence
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Deflection yoke (DY) is one of the main components of the color display tube (CDT) that determines the image quality of a computer monitor. Once a DY anomaly is found during production, the remedy process is performed in two steps: identifying the type of anomaly from the observed problem pattern and adjusting manufacturing process parameters to rectify it. To support this process, we introduce a knowledge-based system using a hybrid knowledge acquisition technique and case-based reasoning. The initial phase of the knowledge acquisition employs a systematic and quantitative data processing including stepwise regression and an inductive learning algorithm. This automated expertise elicitation produces strategies, which are represented by decision trees or if-then rules, to specify DY anomalies from display patterns. The strategies are then refined by introducing human expertise. The knowledge acquisition process was designed to support for this cognitive cooperation. For coordinating the process parameters to remedy the specified anomalies, a case-based reasoning is utilized. The laboratory and field test proved that the developed knowledge-based system could produce highly effective decisions for the process control in DY production.