Abstract. This paper presents relevancy constraints used in subgroup discovery and a novel interpretation of the concept of relevancy in the ROC space context. It provides definitions of feature relevancy and constraints for feature filtering, introduces relevancy based mechanisms for handling of missing values in the examples, and discusses the concept of relevancy as an approach that can help to avoid overfitting. It is argued that logical combinations of features (rules) can be also treated as features and that the same relevancy relations and constraints can be applied for them as well. The paper includes an experimental evaluation of the discussed concepts on a descriptive induction task of gene expression data analysis.
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