Feature selection attempts to capture the more discriminative features and plays a significant
role in multilabel learning. As an efficient mathematical tool to handle incomplete and …

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 …

Abstract Nowadays, Multi-Label Feature Selection (MLFS) attracts more and more attention
to tackle the high-dimensional problem in multi-label data. A key characteristic of existing …

For multilabel classification, the correlations among labels of samples are always ignored by
existing feature selection models, which results in inefficient predictions. In addition, the …

Researchers have considered multi-label learning because of its presence in various real-
world applications, in which each entity is associated with more than one class label. Since …

Multi-label feature selection can efficiently handle large amounts of multi-label data.
However, two pressing issues remain in sparse learning for multi-label data. First, many …

Feature selection can alleviate the problem of the curse of dimensionality by selecting more
discriminative features, which plays an important role in multi-label learning. Recently …

Recently, some existing feature selection approaches neglect the correlation among labels,
and almost manifold-based multilabel learning models do not considered the relationship …

High dimensional and small samples characterize gene expression data and contain a large
number of genes unrelated to disease. Feature selection improves the efficiency of disease …

Hierarchical feature selection has proven to be significant in reducing classification difficulty.
Many existing hierarchical feature selection methods use the hierarchy in the class space as …