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- W1966366162 abstract "This work promotes a novel point of view in rough set applications: rough sets rule learning for ordinal prediction is based on rough graphical representation of the rules. Our approach tackles two barriers of rule learning. Unlike in typical rule learning, we construct ordinal prediction with a mathematical approach, rough sets, rather than purely rule quality measures. This construction results in few but significant rules. Moreover, the rules are given in terms of ordinal predictions rather than as unique values. This study also focuses on advancing rough sets theory in favor of soft-computing. Both theoretical and a designed architecture are presented. The features of our proposed approach are illustrated using an experiment in survival analysis. A case study has been performed on melanoma data. The results demonstrate that this innovative system provides an improvement of rule learning both in computing performance for finding the rules and the usefulness of the derived rules." @default.
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- W1966366162 date "2006-12-01" @default.
- W1966366162 modified "2023-09-25" @default.
- W1966366162 title "Rule learning: Ordinal prediction based on rough sets and soft-computing" @default.
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- W1966366162 doi "https://doi.org/10.1016/j.aml.2005.08.004" @default.
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