Variable precision rough set model
Journal of Computer and System Sciences
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Classification of Gene Expression Data in an Ontology
ISMDA '01 Proceedings of the Second International Symposium on Medical Data Analysis
A New Rough Set Approach to Multicriteria and Multiattribute Classification
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Supervised learning in the gene ontology part i: a rough set framework
Transactions on Rough Sets IV
Supervised learning in the gene ontology part II: a bottom-up algorithm
Transactions on Rough Sets IV
A framework for reasoning with rough sets
Transactions on Rough Sets IV
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Prediction of gene function from expression profiles introduces a new learning problem where the decision classes associated with the objects (i.e., genes) are organized in a directed acyclic graph (DAG). Standard learning methods such a Rough Sets assume that these classes are unrelated, and cannot handle this problem properly. To this end, we introduce an extended rough set framework with several new operators. We show how these operators can be used in an new learning algorithm.