Machine intelligence 12
C4.5: programs for machine learning
C4.5: programs for machine learning
Supporting Start-to-Finish Development of Knowledge Bases
Machine Learning
Automated Support for Building and Extending Expert Models
Machine Learning
Machine Learning
Machine Learning Approaches to Estimating Software Development Effort
IEEE Transactions on Software Engineering
Applications of machine learning and rule induction
Communications of the ACM
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Learning and decision-making in the framework of fuzzy lattices
New learning paradigms in soft computing
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Applying classification algorithms in practice
Statistics and Computing
Knowledge discovery from data?
IEEE Intelligent Systems
Domain knowledge to support the discovery process: previously discovered knowledge
Handbook of data mining and knowledge discovery
Industry: using decision tree induction to minimize process delays in the printing industry
Handbook of data mining and knowledge discovery
Behind-the-scenes data mining: a report on the KDD-98 panel
ACM SIGKDD Explorations Newsletter
Bootstrapping rule induction to achieve rule stability and reduction
Journal of Intelligent Information Systems
Backward chaining rule induction
Intelligent Data Analysis - Selected papers from IDA2005, Madrid, Spain
Flexibly exploiting prior knowledge in empirical learning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
An investigation of TREPAN utilising a continuous oracle model
International Journal of Data Analysis Techniques and Strategies
Searching for meaningful feature interactions with backward-chaining rule induction
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
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Printers are always seeking higher productivity by increasing their production rates and minimizing process delays. When process delays have known causes, they can be mitigated by acquiring causal rules from human experts and then applying sensors and automated real-time diagnostic devices to the process. However, for some delays the experts have only weak causal knowledge or none at all. In such cases, machine learning tools can collect training data and process it through an induction engine in search of diagnostic knowledge. We have applied a machine learning strategy known as decision tree induction to derive a set of rules about a long-standing problem in rotogravure printing. The induction mechanism is embedded within a knowledge acquisition system that suggests plausible rules to an expert, who can override the rules or modify the data from which the rules were derived. By using decision tree induction to derive process control rules, this system lets experts participate in knowledge acquisition by doing what they do best: exercising their expertise.