Abstract
In the classical rough set approaches, lower approximations of single decision classes have been mainly treated. Based on those approximations, attribute reduction and rule induction have been developed. In this paper, from the authors’ recent studies, we demonstrate that various analyses are conceivable by treating lower approximations of unions of multiple decision classes.
This work was partially supported by JSPS KAKENHI Grant Number 26350423.
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Inuiguchi, M. (2016). Rough Set Approaches to Imprecise Modeling. In: Flores, V., et al. Rough Sets. IJCRS 2016. Lecture Notes in Computer Science(), vol 9920. Springer, Cham. https://doi.org/10.1007/978-3-319-47160-0_5
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DOI: https://doi.org/10.1007/978-3-319-47160-0_5
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