Curse of dimensionality is known as big challenges in data mining, pattern recognition,
computer vison and machine learning in recent years. Feature selection and feature …

Multi-label feature selection is an efficient technique to deal with the high dimensional multi-
label data by selecting the optimal feature subset. Existing researches have demonstrated …

As an effective method to deal with the curse of dimensionality, multi-label feature selection
aims to select the most representative subset of features by eliminating unfavorable features …

Multilabel feature selection solves the dimension distress of high-dimensional multilabel
data by selecting the optimal subset of features. Noisy and incomplete labels of raw …

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 …

Due to the rapid growth of labels and high-dimensional data, multi-label feature selection
has attracted increasing attention. However, two common issues are ignored by existing …

Feature selection is a key technique to tackle the curse of dimensionality in multi-label
learning. Lots of embedded multi-label feature selection methods have been developed …

The high dimensionality and complexity of multi-label data make obtaining accurate label
sets in practical applications difficult. Noisy data in the labels will affect the model's …

Prevalent multi-label feature selection (MLFS) approaches to obtain the most suitable
feature subset by dealing with two issues, namely sparsity and redundancy. In this paper, we …

Feature selection is one of the key steps in building a predictive model in multi-label
classification. However, most of the existing methods do not take into account information …