Unknown attribute values in induction
Proceedings of the sixth international workshop on Machine learning
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Machine Learning
Induction of comprehensible models for gene expression datasets by subgroup discovery methodology
Journal of Biomedical Informatics - Special issue: Biomedical machine learning
Expert-guided subgroup discovery: methodology and application
Journal of Artificial Intelligence Research
Relevancy in constraint-based subgroup discovery
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Journal of Intelligent Information Systems
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Rule learning systems use features as the main building blocks for rules. A feature can be a simple attribute-value test or a test of the validity of a complex domain knowledge relationship. Most existing concept learning systems generate features in the rule construction process. However, the separation of feature generation and rule construction processes has several theoretical and practical advantages. In particular, the proposed transformation from the attribute to the feature space motivates a novel, theoretically justified procedure for handling of unknown attribute values. This approach suggests also a novel procedure for handling imprecision of numerical attributes. The possibility of controlling the expected imprecision of numerical attributes during the induction process is a novel machine learning concept which has a high application potential for solving real world problems.