Nowadays, being in digital era the data generated by various applications are increasing
drastically both row-wise and column wise; this creates a bottleneck for analytics and also …

Feature selection, as a data preprocessing strategy, has been proven to be effective and
efficient in preparing data (especially high-dimensional data) for various data-mining and …

Recently, ransomware attacks have been among the major threats that target a wide range
of Internet and mobile users throughout the world, especially critical cyber physical systems …

This survey aims at providing a state-of-the-art overview of feature selection and fusion
strategies, which select and combine multi-view features effectively to accomplish …

Although demonstrating great success, previous multi-view unsupervised feature selection
(MV-UFS) methods often construct a view-specific similarity graph and characterize the local …

The massive growth of data in recent years has led challenges in data mining and machine
learning tasks. One of the major challenges is the selection of relevant features from the …

Fisher score is one of the most widely used supervised feature selection methods. However,
it selects each feature independently according to their scores under the Fisher criterion …

Compared with supervised learning for feature selection, it is much more difficult to select
the discriminative features in unsupervised learning due to the lack of label information …

Feature selection has aroused considerable research interests during the last few decades.
Traditional learning-based feature selection methods separate embedding learning and …

With the widespread usage of social networks, forums and blogs, customer reviews
emerged as a critical factor for the customers' purchase decisions. Since the beginning of …