In the past decades, the rapid growth of computer and database technologies has led to the
rapid growth of large-scale datasets. On the other hand, data mining applications with high …

Intrusion detection systems (IDSs) play an important role in the security of computer
networks. One of the main challenges in IDSs is the high-dimensional input data analysis …

Feature subset selection is a major problem in data mining which can help to reduce
computation time, improve prediction performance, and build understandable models …

In many real-world unsupervised learning applications, given data with balanced
distribution, that is, there are an approximately equal number of instances in each class, we …

Unsupervised feature selection methods try to select features which can well preserve the
intrinsic structure of data. To represent such structure, conventional methods construct …

Feature selection, both for supervised as well as for unsupervised classification is a relevant
problem pursued by researchers for decades. There are multiple benchmark algorithms …

The recent advance in the microarray data analysis makes it easy to simultaneously
measure the expression levels of several thousand genes. These levels can be used to …

Most state-of-the-art feature selection methods tend to overlook the structural relationship
between a pair of samples associated with each feature dimension, which may encapsulate …

Typical 'omic analyses reduce complex biological systems to simple lists of supposedly
independent variables, failing to account for changes in the wider transcriptional landscape …

The impact of internet and information systems across various domains have resulted in
substantial generation of multidimensional datasets. The use of data mining and knowledge …