Abstract
Rough set is a generalization of crisp rough set to deal with data sets with real value attributes. Attribute reduction is very important in rough set-based data analysis because it can be used to simplify the induced decision rules without reducing the classification accuracy. The notion of reduct plays a key role in rough set-based attribute reduction. In rough set theory, a reduct is generally defined as a minimal subset of attributes that can classify the same domain of objects as unambiguously as the original set of attributes. Experimental results imply that our algorithm of attribute reduction with distribution of electricity feasible and valid.
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Ren, D. (2011). Application of Attributes Reduction Based on Rough Set in Electricity Distribution. In: Liu, C., Chang, J., Yang, A. (eds) Information Computing and Applications. ICICA 2011. Communications in Computer and Information Science, vol 244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27452-7_25
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DOI: https://doi.org/10.1007/978-3-642-27452-7_25
Publisher Name: Springer, Berlin, Heidelberg
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