The rough set model has two symmetry approximations called upper approximation and lower approximation, which correspond to a concept’s intension and extension, respectively. Multi-label learning enforces the rough set model, which wants to be applied considering the correlations among labels, while the target concept should not be limited to only one. This paper proposes a multi-target model considering label correlation (Neighborhood Multi-Target Rough Sets, NMTRS) and proposes an attribute reduction approach based on NMTRS. First, some definitions of NMTRS are introduced. Second, some properties of NMTRS are discussed. Third, some discussion about the attribute significance measure is given. Fourth, the attribute reduction approaches based on NMTRS are proposed. Finally, the efficiency and validity of the designed algorithms are verified by experiments. The experiments show that our algorithm shows considerable performance when compared to state-of-the-art approaches.

中文翻译：

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基于邻域多目标粗糙集的多标签属性约简

粗糙集模型有两个对称近似，称为上近似和下近似，它们分别对应于概念的内涵和外延。多标签学习强制使用粗糙集模型，考虑到标签之间的相关性，而目标概念不应仅限于一个。本文提出了一种考虑标签相关性的多目标模型（Neighborhood Multi-Target Rough Sets，NMTRS），并提出了一种基于NMTRS的属性约简方法。首先，介绍了 NMTRS 的一些定义。其次，讨论了 NMTRS 的一些特性。第三，对属性显着性测度进行了讨论。第四，提出了基于NMTRS的属性约简方法。最后，通过实验验证了所设计算法的有效性和有效性。实验表明，与最先进的方法相比，我们的算法显示出相当大的性能。