In the multi-view multi-label (MVML) classification problem, multiple views are
simultaneously associated with multiple semantic representations. Multi-view multi-label …

Multi-label learning deals with cases where every instance corresponds to multiple labels.
The objective is to learn mapping from an instance to a relevant label set. Existing multi …

We propose a sentiment classification method for large scale microblog text based on the
attention mechanism and the bidirectional long short-term memory network (SC-ABiLSTM) …

Textual emotion detection is an attractive task while previous studies mainly focused on
polarity or single-emotion classification. However, human expressions are complex, and …

Multi-label datasets often contain label information with missing values and recovering them
is a non-trivial challenge. Several methods augment the observed label matrix by …

In multi-view multi-label learning, each object is represented by multiple heterogeneous
data and is simultaneously associated with multiple class labels. Previous studies usually …

Feature selection used for dimensionality reduction of the feature space plays an important
role in multi-label learning where high-dimensional data are involved. Although most …

Multi-label text classification (MLTC) is a significant task that aims to assign multiple labels to
each given text. There are usually correlations between the labels in the dataset. However …

Label-specific features learning is a multi-label learning framework that utilizes label feature
extraction to solve a single example where multiple class labels exist simultaneously. As an …

In recent years, it has been difficult for multilabel classification to obtain complete multilabel
data in real‐world applications, and even a large number of labels for training samples are …