Instance-Based Learning Algorithms
Machine Learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Machine learning and data mining
Communications of the ACM
On Issues of Instance Selection
Data Mining and Knowledge Discovery
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
Likelihood-Based Data Squashing: A Modeling Approach to Instance Construction
Data Mining and Knowledge Discovery
A Unifying View on Instance Selection
Data Mining and Knowledge Discovery
Integrating feature and instance selection for text classification
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Peculiarity Oriented Multidatabase Mining
IEEE Transactions on Knowledge and Data Engineering
Detecting Interesting Exceptions from Medical Test Data with Visual Summarization
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
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In this paper, we propose a method which removes exceptional patients in a spiral manner for obtaining a definition of a disease in the form of a table of conditional probabilities and we describe its application to chronic hepatitis data. The removal is based on a risk-ratio-based criterion and can be supported by our previously developed data mining methods and medical experts. A series of experiments in which two domain experts decided exceptional patients to be removed show that our proposed method is effective and promising from various viewpoints such as obtaining new hypotheses and improving skills of domain experts. Another series of experiments in which exceptional patients were removed automatically led us to a rediscovery of a piece of knowledge, which had been reported in an article of a medical journal as the main result of the article.