近邻标签空间非平衡化标签补全的多标签学习

研究者目前通常通过标注标签之间的相关信息研究标签之间的相关性,未考虑未标注标签与标注标签之间的关系对标签集质量的影响.受K近邻的启发,文中提出近邻标签空间的非平衡化标签补全算法(NeLC-NLS),旨在充分利用近邻空间中元素的相关性,提升近邻标签空间的质量,从而提升多标签分类性能.首先利用标签之间的信息熵衡量标签之间关系的强弱,获得基础标签置信度矩阵.然后利用提出的非平衡标签置信度矩阵计算方法,获得包含更多信息的非平衡标签置信度矩阵.继而度量样本在特征空间中的相似度,得到k个近邻标签空间样本,并利用非平衡标签置信度矩阵计算得到近邻标签空间的标签补全矩阵.最后利用极限学习机作为线性分类器进行分类.在公开的8个基准多标签数据集上的实验表明,NeLC-NLS具有一定优势,使用假设检验和稳定性分析进一步说明算法的有效性.

Multi-label Learning for Non-equilibrium Labels Completion in Neighborhood Labels Space

The correlation between labels are studied through the related information about the marked labels. However, the influence of the relationship between unmarked and marked labels on the quality of the multi-label set is not taken into account. Inspired by k-nearest neighbors(KNN), a non-equilibrium labels completion of neighboring labels space(NeLC-NLS) is proposed to improve the quality of the neighboring label space and the performance of the multi-label classification. Firstly, the information entropy between labels is utilized to measure the strength of the relationship between labels, and the confidence matrix of the basic label is obtained. Then, the confidence matrix of non-equilibrium labels containing more information is obtained via the proposed non-equilibrium label confidence matrix. Secondly, the similarity of samples is measured in the feature space and the k-nearest neighbors are obtained. Then, the non-equilibrium labels completion matrix is employed to calculate the label completion matrix of the neighboring labels space. Finally, the extreme learning machine is adopted as a linear classifier. The experimental results of the proposed algorithm on 8 public multi-label datasets show that NeLC-NLS is superior to other multi-label learning algorithms. The effectiveness of NeLC-NLS is further illustrated by using hypothesis testing and stability analysis.