Feature Selection (FS) methods alleviate key problems in classification procedures as they
are used to improve classification accuracy, reduce data dimensionality, and remove …

In many real-world multi-label applications, the content of multi-label data is usually
characterized by high dimensional features, which contains complex correlation information …

In multi-label learning, objects are essentially related to multiple semantic meanings, and
the type of data is confronted with the impact of high feature dimensionality simultaneously …

Recently, multilabel classification has generated considerable research interest. However,
the high dimensionality of multilabel data incurs high costs; moreover, in many real …

Hierarchical multi-label text classification (HMTC) is a fundamental but challenging task of
numerous applications (eg, patent annotation), where documents are assigned to multiple …

The task of multi-label feature selection (MLFS) is to reduce redundant information and
generate the optimal feature subset from the original multi-label data. A variety of MLFS …

Feature selection is able to select more discriminative features for classification and plays an
important role in multi-label learning to alleviate the effect of the curse of dimensionality …

Nowadays, multi-label learning is ubiquitous in practical applications, in which multi-label
data is always confronted with the curse of high-dimensional features. Feature selection has …

In various application domains, high-dimensional multi-label data has become more
prevalent, presenting two significant challenges: instances with high-dimensional features …

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 …