Multi-level information fusion for missing multi-label learning based on stochastic concept clustering

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.