In the multi-label classification task, an instance is simultaneously associated with multiple
semantic labels. Due to the high complexity of the semantic space in practical applications …

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 …

As a cognitive process, concept-cognitive learning (CCL) emphasizes the structured
expression of data through systematic cognition and understanding, to obtain valuable …

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 …

With the development of new sequencing technologies, availability of genomic data has
grown exponentially. Over the past decade, numerous studies have used genomic data to …

Presently, multi-label classification algorithms are mainly based on positive and negative
logical labels, which have achieved good results. However, logical labeling inevitably leads …

Label missing and class imbalance problems are two hot research topics in machine
learning, and they have been impeding the improvement of model performance, especially …

Limited-supervised multi-label learning (LML) leverages weak or noisy supervision for multi-
label classification model training over data with label noise, which contain missing labels …

Extreme Multi Label (XML) problems, and in particular XML completion--the task of
prediction the missing labels of an entity--have attracted significant attention in the past few …

Since multilabel text classification datasets often face the problem of label imbalance,
therefore, using either sequence-based deep learning (DL) model or graph neural network …