Selective label enhancement for multi-label classification based on three-way decisions.

Multi-label classification is a challenging issue in the data science community due to the ambiguity of label semantics. Existing studies mainly focus on improving label association with logical labels, but the performance suffers from the threshold setting. Although label distribution learning gains superior discrimination, the expenditure of collecting large-scale fine-grained numerical labels is intolerable. To address the uncertainty of logical label semantics, we propose a novel model called three-way decisions with label enhancement (3WDLE). For unseen instances, we implement a trisecting-acting-outcome framework. In the trisecting stage, an uncertainty measure called global uncertain-prone degree partitions these instances into uncertain and certain regions, where the trisecting procedure is completed from label level to instance level by leveraging the distributions of pseudo-label information. In the acting stage, instances recognized as certain regions directly take the results generated by label-specific learning, whereas the remaining are reclassified by conducting selective label enhancement. The enriched knowledge generated by the label enhancement module is learnt on trustworthy instances only. In the outcome stage, we adopt five evaluation metrics to evaluate the classification performance from the perspectives of both labels and instances. In this way, three-way decisions provide a systematic methodology to deal with uncertainty in multi-label classification, which combines logical label learning with numerical label learning into a unified framework to optimize the performance of the multi-label classification model. Extensive experiments demonstrate the superiority of 3WDLE over state-of-the-art multi-label classifications with logical labels only. [ABSTRACT FROM AUTHOR]