Missing multi-label learning is to address the problem of missing labels in multi-label datasets for multi-label classification tasks. Notably, the complex dependencies that typically exist between labels make accurate classification particularly challenging in the presence of missing labels. Some existing missing multi-label classification models often utilize feature selection to effectively recognize the dependencies between labels and features. However, they are ineffective at capturing hierarchical relationships of feature information, probably leading to a decline in prediction performance. To address this problem, this paper proposes a missing multi-label classification model based on multi-level stochastic concept clustering (MML-MSCC) to make dependencies between features and labels recognized more accurately and prediction performance better. In our model, optimal granularity selection is achieved through the global mutual information between features and labels, which makes the study of stochastic granule concept across multiple granularities. Furthermore, we utilize a stochastic concept clustering method to combine similar feature information for the purpose of making the missing label completion more reasonable. Note that stochastic granule concept clustering is performed with cross-granularity, thereby effectively capturing hierarchical relationships among feature information. Finally, to evaluate the performance of our model, we compare the MML-MSCC model with 9 existing missing multi-label classification models on 12 open datasets in terms of six evaluation metrics.

中文翻译：

---

基于随机概念聚类的缺失多标签学习多层次信息融合


缺失多标签学习是为了解决多标签分类任务中多标签数据集中标签缺失的问题。值得注意的是，标签之间通常存在的复杂依赖关系使得在标签缺失的情况下进行准确分类特别具有挑战性。一些现有的缺失多标签分类模型经常利用特征选择来有效识别标签和特征之间的依赖关系。但是，它们在捕获特征信息的分层关系方面无效，可能导致预测性能下降。针对这一问题，文中提出了一种基于多级随机概念聚类（MML-MSCC）的缺失多标签分类模型，以更准确地识别特征和标签之间的依赖关系，提高预测性能。在我们的模型中，最优粒度选择是通过特征和标签之间的全局互信息来实现的，这使得跨多个粒度的随机颗粒概念的研究成为可能。此外，我们利用随机概念聚类方法将相似的特征信息组合在一起，以使缺失标签的补全更加合理。请注意，随机颗粒概念聚类是以跨粒度方式执行的，从而有效地捕获特征信息之间的分层关系。最后，为了评估我们模型的性能，我们将 MML-MSCC 模型与 12 个开放数据集上 9 个现有的缺失多标签分类模型在 6 个评估指标方面进行了比较。