Jiang Jiansheng, Ge Wenxin, Wang Yibin, Cheng Yusheng, Xu Yuting
School of Computer and Information, Anqing Normal University, Anqing, 246133, China.
School of Intelligence and Electrical Engineering, Huainan Vocational Technical College, Huainan, 232001, China.
Sci Rep. 2024 Oct 4;14(1):23099. doi: 10.1038/s41598-024-72765-6.
Presently, multi-label classification algorithms are mainly based on positive and negative logical labels, which have achieved good results. However, logical labeling inevitably leads to the label misclassification problem. In addition, missing labels are common in multi-label datasets. Recovering missing labels and constructing soft labels that reflect the mapping relationship between instances and labels is a difficult task. Most existing algorithms can only solve one of these problems. Based on this, this paper proposes a soft-label recover based label-specific features learning (SLR-LSF) to solve the above problems simultaneously. Firstly, the information entropy is used to calculate the confidence matrix between labels, and the membership degree of soft labels is obtained by combining the label density information. Secondly, the membership degree and confidence matrix are combined to construct soft labels, and this process not only solves the problem of missing labels but also obtains soft labels with richer semantic information. Finally, in the process of learning specific label features for soft labels. The local smoothness of the labels learned through stream regularization is complemented by the global label correlation, thus improving the classification performance of the algorithm. To demonstrate the effectiveness of the proposed algorithm, we conduct comprehensive experiments on several datasets.
目前，多标签分类算法主要基于正负逻辑标签，已取得了良好的效果。然而，逻辑标注不可避免地会导致标签误分类问题。此外，多标签数据集中缺失标签的情况很常见。恢复缺失标签并构建反映实例与标签之间映射关系的软标签是一项艰巨的任务。大多数现有算法只能解决这些问题中的一个。基于此，本文提出了一种基于软标签恢复的标签特定特征学习（SLR-LSF）方法，以同时解决上述问题。首先，利用信息熵计算标签之间的置信矩阵，并结合标签密度信息得到软标签的隶属度。其次，将隶属度和置信矩阵相结合来构建软标签，这一过程不仅解决了缺失标签的问题，还获得了具有更丰富语义信息的软标签。最后，在为软标签学习特定标签特征的过程中，通过流正则化学习到的标签的局部平滑性与全局标签相关性相互补充，从而提高了算法的分类性能。为了证明所提算法的有效性，我们在几个数据集上进行了全面的实验。
