Abstract
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.
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Midelfart, H., Komorowski, J. (2002). A Rough Set Framework for Learning in a Directed Acyclic Graph. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds) Rough Sets and Current Trends in Computing. RSCTC 2002. Lecture Notes in Computer Science(), vol 2475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45813-1_18
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DOI: https://doi.org/10.1007/3-540-45813-1_18
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