Learning label-specific features is an effective strategy for multi-label classification. Existing
multi-label classification methods for learning label-specific features face two challenges …

Different from single-label learning, multi-label learning has rich semantic information. Label
embedding obtains the inherent intelligence of the label space by projecting the label space …

In multi-label learning problems, the class labels are correlated and the label correlations
can be leveraged to improve the predictive performance of a classifier. Methods that …

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 …

Emotion recognition has been used widely in various applications such as mental health
monitoring and emotional management. Usually, emotion recognition is regarded as a text …

Available works addressing multi-label classification in a data stream environment focus on
proposing accurate prediction models; however, they struggle to balance effectiveness and …

The problem of multi-label classification with missing labels (MLML) is a common challenge
that is prevalent in several domains, eg image annotation and auto-tagging. In multi-label …

Feature selection is one of the important pre-processing methods for dimensionality
reduction in multi-label learning tasks, which has attracted extensive attention in recent …

Multi-view multi-label learning is widely used in multiple fields, and it aims to process data
sets represented by multiple forms (views) and labeled by multiple classes. But most real …

For remote sensing (RS) scene classification, most of the existing techniques annotate a
scene image with merely a single semantic label. However, with the recent advance of …