As a well-known multi-label classification method, the performance of ML-KNN may be affected by the uncertainty knowledge from samples. The rough set theory acts as an effective tool for data uncertainty analysis, which can identify the samples easy to cause misclassification in the learning process. In this paper, a hybrid framework by fusing rough sets with ML-KNN for multi-label learning is proposed, whose main idea is to depict easy misclassified samples by rough sets and to measure the discernibility of attributes for such samples. First, a rough set model titled NRFD_RS based on neighborhood relations and fuzzy decisions is proposed for multi-label data to find the heterogeneous sample pairs generated from the boundary regions of each label. Then, the weight of an attribute is defined by evaluating its discernibility to those heterogeneous sample pairs. Finally, a weighted HEOM distance is reconstructed and utilized to ML-KNN. Comprehensive experimental results with fourteen public multi-label data sets, including ten regular-scale and four larger-scale data sets, verify the effectiveness of the proposed framework relative to several state-of-the-art multi-label classification methods.

作为一种众所周知的多标签分类方法，ML-KNN 的性能可能会受到来自样本的不确定性知识的影响。粗糙集理论作为数据不确定性分析的有效工具，可以在学习过程中识别出容易导致误分类的样本。本文提出了一种将粗糙集与 ML-KNN 融合用于多标签学习的混合框架，其主要思想是通过粗糙集描述容易误分类的样本，并衡量这些样本的属性可辨别性。首先，针对多标签数据提出了一种基于邻域关系和模糊决策的粗糙集模型 NRFD_RS，以找到从每个标签的边界区域生成的异构样本对。然后，属性的权重是通过评估其对那些异质样本对的可辨别性来定义的。最后，重建加权 HEOM 距离并将其用于 ML-KNN。14 个公共多标签数据集的综合实验结果，包括 10 个常规规模数据集和 4 个更大规模数据集，验证了所提出的框架相对于几种最先进的多标签分类方法的有效性。