A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Knowledge reuse among diagnostic problem-solving methods in the shell-kit D3
International Journal of Human-Computer Studies
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: concepts and techniques
Data mining: concepts and techniques
Machine Learning
Inductive Learning for Case-Based Diagnosis with Multiple Faults
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
An Efficient Algorithm for Deriving Compact Rules from Databases
Proceedings of the 4th International Conference on Database Systems for Advanced Applications (DASFAA)
Handbook of data mining and knowledge discovery
Expert-guided subgroup discovery: methodology and application
Journal of Artificial Intelligence Research
How to combine CBR and RBR for diagnosing multiple medical disorder cases
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
A knowledge-based clinical toxicology consultant for diagnosing multiple exposures
Artificial Intelligence in Medicine
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Semi-automatic data mining approaches often yield better results than plain automatic methods, due to the early integration of the user’s goals. For example in the medical domain, experts are likely to favor simpler models instead of more complex models. Then, the accuracy of discovered patterns is often not the only criterion to consider. Instead, the simplicity of the discovered knowledge is of prime importance, since this directly relates to the understandability and the interpretability of the learned knowledge. In this paper, we present quality measures considering the understandability and the accuracy of (learned) rule bases. We describe a unifying quality measure, which can trade-off small losses concerning accuracy vs. an increased simplicity. Furthermore, we introduce a semi-automatic data mining method for learning understandable and accurate rule bases. The presented work is evaluated using cases from a real world application in the medical domain.