With the rapid advancement of big data technology, high-dimensional datasets comprising
multi-label data have become prevalent in various fields. However, these datasets often …

In multi-label classification, many existing works only pay attention to the label-specific
features and label correlation while they ignore the common features and instance …

Label-specific features serve as an effective strategy to learn from multi-label data, where a
set of features encoding specific characteristics of each label are generated to help induce …

In multi-label classification, the strategy of label-specific features has been shown to be
effective to learn from multi-label examples by accounting for the distinct discriminative …

文本分类(TC) 是自然语言处理(NLP) 领域的重要基础任务, 多标签文本分类(MLTC) 是TC
的重要分支. 为了对多标签文本分类领域进行深入了解, 介绍了多标签文本分类的概念和流程 …

The multilabel twin support vector machine (MLTSVM) has been widely applied to multilabel
classification fields because of its excellent classification performance, but it has the …

Existing multi-label learning classification algorithms ignore that class labels may be
determined by some features in the original feature space. And only a partial label of each …

Multi-label algorithms often use an identical feature space to build classification models for
all labels. However, labels generally express different semantic information and should have …

In multi-label classification, the task is to induce predictive models which can assign a set of
relevant labels for the unseen instance. The strategy of label-specific features has been …

Label-specific features work as an effective supervised feature manipulation strategy to
account for distinct discriminative properties of each class label in multi-label classification …