IEEE Trans Cybern. 2017 Sep;47(9):2460-2471. doi: 10.1109/TCYB.2016.2636339. Epub 2016 Dec 20.
Attribute selection is considered as the most characteristic result in rough set theory to distinguish itself to other theories. However, existing attribute selection approaches can not handle partially labeled data. So far, few studies on attribute selection in partially labeled data have been conducted. In this paper, the concept of discernibility pair based on rough set theory is raised to construct a uniform measure for the attributes in both supervised framework and unsupervised framework. Based on discernibility pair, two kinds of semisupervised attribute selection algorithm based on rough set theory are developed to handle partially labeled categorical data. Experiments demonstrate the effectiveness of the proposed attribute selection algorithms.
属性选择被认为是粗糙集理论最具特色的结果，使其有别于其他理论。然而，现有的属性选择方法无法处理部分标记数据。到目前为止，关于部分标记数据中的属性选择的研究还很少。本文提出了基于粗糙集理论的可区分对的概念，以构建监督框架和无监督框架中属性的统一度量。基于可区分对，提出了两种基于粗糙集理论的半监督属性选择算法，用于处理部分标记的分类数据。实验证明了所提出的属性选择算法的有效性。
