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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