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 …

Environmental biomonitoring techniques have been widely applied to assess the quality
states of toxic chemical compounds in surface freshwater quality. The methods based on …

An orthogonal representation of features can offer valuable insights into feature selection as
it aims to find a representative subset of features in which all features can be accurately …

Feature selection techniques are widely being used as a preprocessing step to train
machine learning algorithms to circumvent the curse of dimensionality, overfitting, and …

The performance of machine learning algorithms can be affected by redundant features of
high-dimensional data. Furthermore, these irrelevant features increase the time of …

Feature selection is a highly regarded research area in the field of data mining, as it
significantly enhances the efficiency and performance of high-dimensional data analysis by …

Unbalanced incomplete multiview data are widely generated in engineering areas due to
sensor failures, data acquisition limitations, etc. However, current research works are rarely …

Feature selection (FS) is essential in machine learning and data mining as it makes
handling high-dimensional data more efficient and reliable. More attention has been paid to …

Unsupervised feature selection (UFS) has attracted increasing attention because of the
difficulty and high cost of obtaining data labels. Since the ignorance of redundancy between …

Feature selection is to identify informative and concise sub-features from raw datasets,
which can be modelled as an optimization issue. An adaptive ranking moth-flame …