中文摘要：

邻域粗糙集模型中,随着信息粒尺寸的增长,基于多数投票原则的邻域分类器（NC）容易对未知样本的类别产生误判。为了缓解该问题,在协同表达分类（CRC）思想的基础上,提出了一种基于邻域协同表达的分类方法,即邻域协同分类器（NCC）。NCC首先借助邻域粗糙集模型对分类学习任务进行特征选择,然后找出被选特征下未知样本的邻域空间,最后在邻域空间内采用协同表达来代替多数投票原则,找出与未知样本具有最小重构误差的类别作为预测的类别标记。在4组UCI数据集上的实验结果表明：1）与NC相比,所提NCC在大尺寸信息粒下获得了较为满意的分类效果;2）与CRC相比,所提NCC在保持良好分类精度的同时,极大地降低了字典样本的规模,进而提高了分类的效率。

英文摘要：

In the neighborhood rough set model, with the increasing of the size of information granules, the majority vo- ting rule based neighborhood classifier （NC） is easy to misjudge the classes of unknown samples. To remedy this defi- ciency,based on the idea of collaborative representation based classification （CRC）, we proposed a neighborhood colla- borative representation based classification method, namely, the neighborhood collaborative classifier （NCC）. NCC first ly performs feature selection in the classification learning task with neighborhood rough set model, and then finds the neighborhood space of unknown sample under selected features. Finally, instead of the majority voting rule in the neigh- borhood space, NCC judges the class of unknown sample with the collaborative representation, which considers the class with the minimal reconstruction error for unknown sample as the predicted category. Experimental results on 4 UCI da- ta sets show that compared with NC, the proposed NCC achieves satisfactory performance in larger information granules and compared with CRC, and the proposed NCC greatly reduces the size of the dictionary while maintaining good classi fication accuracy,and improves the efficiency of classification.