For the problem that most existing classification methods based on neighborhood covering directly classify samples according to the nearest neighborhood. The certain classification methods do not consider the differences between each neighborhood, which may lead to classification mistakes. Local density is introduced to characterize the differences between each neighborhood, and a classification method by integrating density and neighborhood covering reduction(D-NCR) is proposed in this paper. First, the redundant neighborhoods are eliminated by the neighborhood covering reduction. Then, the weighted distance from the test sample to the neighborhood center is defined through local density of neighborhood center. Finally, two classification strategies are proposed for different test samples based on the weighted distance. Experimental results on UCI data sets demonstrate the effectiveness of the proposed method.

neighborhood rough set; neighborhood covering reduction; local density; weighted distance; classification strategy