MLRUI-R: Multilabel Feature Selection Considering Relative Uncertainty Information
针对现有粒计算方法假设所有标签同等重要、无法捕捉特征在标签空间中的相对识别能力的问题，提出一种基于粗糙集分析标签依赖、引入相对不确定性信息的多标签特征选择方法，实验验证了有效性。
Granular computing, which simulates human cognition by partitioning objects into multiple granules, serves as a valuable approach for handling data with uncertainty and has been widely applied to multilabel feature selection. However, existing methods based on granular computing typically assume equal importance across all labels, which limits their ability to capture the relative recognition of features within the label space. In this article, we address this limitation by analyzing label space dependencies through the lens of rough set theory. In addition, a novel method for multilabel feature selection is introduced, which incorporates relative uncertainty information. First, we propose an object grid-based acceleration method to speed up the computation of relationships in the process of constructing neighborhood granularity, and provide a new definition of neighborhood granularity on this basis. Second, based on the constructed neighborhood granularity, we define uncertainty measures under multilabel data and analyze their corresponding theoretical properties. Finally, by analyzing the dependencies within the label space, we derive an importance matrix for the labels and, by combining it with the defined uncertainty measure, develop a multilabel feature selection method that incorporates relative uncertainty. The experimental results validate the effectiveness of the proposed method.