Feature selection (FS), as one of the most significant preprocessing techniques in the fields
of machine learning and pattern recognition, has received great attention. In recent years …

Real-world problems are commonly characterized by a high feature dimensionality, which
hinders the modelling and descriptive analysis of the data. However, some of these data …

In recent years, feature selection for multilabel classification has attracted attention in
machine learning and data mining. However, some feature selection methods ignore the …

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 …

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

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 …

Multi-label feature selection captures a reliable and informative feature subset from high-
dimensional multi-label data, which plays an important role in pattern recognition. In …

High dimensionality problem is an important concern for short text classification due to its
effect on computational cost and accuracy of classifiers. Also, short text data, besides being …

Feature selection approaches aim to select a set of prominent features that best describe the
data to improve the efficiency without degrading the performance of the model. In many real …

The interaction between features can provide essential information that affects the
performances of learning models. Nevertheless, most feature selection methods do not take …