基于标记关系的模糊粗糙集模型

Fuzzy Rough Set Model Based on Label Relations

多标记分类任务中的数据通常是高维的,直接利用高维数据建模可能导致训练效率低下,模型复杂,同时可能影响分类效果.针对多标记数据,文中提出属性-标记矩阵的概念,建立基于标记关系的模糊粗糙集模型,设计此类模型的约简算法,用于多标记数据分类任务的特征选择.在8个公开的数据集上实验验证文中算法的有效性.

The data in multi-label classification tasks are usually high dimensional. Utilizing high-dimension data directly for modeling often results in a lower training efficiency or a complex model with the classifier performance reduced. For multi-label data, the concept of attribute-label matrix is proposed, a label relation based fuzzy rough set model is established, and a reduction algorithm of the model is then designed for feature selection of multi-label classification tasks. Finally, the effectiveness of the proposed method is verified on eight public datasets.