摘要： 多标签模糊数据中存在着特征冗余、交互关系复杂及特征重要度差异大等问题，制约了多标签学习的分类性能。为此，提出ReliefF-β算法对特征赋权，给出基于特征加权交互的多标签特征选择方法。首先，针对多标签模糊数据，构造特征相似度和标签相似度，引入调节参数β融合两类相似度，构建全局样本相似度，提出ReliefF-β算法为特征赋权。其次，基于特征权重引入多标签加权模糊粗糙集，定义加权模糊熵及加权模糊互信息等不确定性度量，研究其性质和关系。接着，综合考虑特征的相关性、冗余性和交互性，定义特征加权评价函数，给出基于特征加权交互的多标签特征选择算法。最后，在两种分类器下对所提算法进行对比实验分析，结果表明，相比其他对比算法，在ML-KNN下，平均精度(AP)平均提升8.79%，汉明损失(HL)、排序损失(RL)、覆盖率(CV)和1-错误率(OE)分别平均降低5.06%、15.33%、10.97%和23.06%；在BRDT下，AP平均提升4.06%，HL、RL、CV和OE分别平均降低8.60%、10.28%、7.19%和5.89%，消融实验与统计检验进一步验证了所提方法的有效性。

Abstract: In multi-label fuzzy data, feature redundancy, complex interaction relationships between features, and unequal feature contributions are commonly present, which affect the classification performance of multi-label learning. To address these issues, ReliefF-β algorithm is proposed to assign feature weights, and a multi-label feature selection method based on feature weighted interaction is presented. Firstly, feature similarity and label similarity are constructed for multi-label fuzzy data. A regulating parameter β is introduced to fuse the two similarities and construct a global sample similarity, then ReliefF-β algorithm is proposed for feature weighting. On this basis, multi-label weighted fuzzy rough set is introduced based on feature weights, and uncertainty measures such as weighted fuzzy entropy and weighted fuzzy mutual information are defined. The related properties and relationships among these measures are studied. Furthermore, a feature weighted evaluation function is defined by considering feature relevance, redundancy, and interaction, then a multi-label feature selection algorithm based on feature weighted interaction is proposed. Finally, comparative experiments are conducted under two classifiers. The results show that, compared with other comparison algorithms, under ML-KNN, the proposed method improves Average Precision (AP) by 8.79% on average, while Hamming Loss (HL), Ranking Loss (RL), Coverage (CV), and One-Error (OE) are reduced by 5.06%, 15.33%, 10.97% and 23.06%, respectively. Under BRDT, AP is improved by 4.06%, and HL, RL, CV, and OE are reduced by 8.60%, 10.28%, 7.19% and 5.89%, respectively. Ablation studies and statistical tests further verify the effectiveness of the proposed method.