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摘要
Double-quantitative rough approximation, containing two types of quantitative information, indicated stronger generalization ability and more accurate data processing capacity than the single-quantitative rough approximation. In this paper,the neighborhood-based double-quantitative rough set models are firstly presented in a set-valued information system. Secondly, the attribute reduction method based on the lower approximation invariant is addressed, and the relevant algorithm for the approximation attribute reduction is provided in the set-valued information system. Finally,to illustrate the superiority and the effectiveness of the proposed reduction approach,experimental evaluation is performed using three datasets coming from the University of California-Irvine(UCI) repository.
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