In recent years, unsupervised feature selection methods have raised considerable interest in
many research areas; this is mainly due to their ability to identify and select relevant features …

Abstract Model-based clustering is a popular tool which is renowned for its probabilistic
foundations and its flexibility. However, high-dimensional data are nowadays more and …

Collagens in the extracellular matrix (ECM) provide a physical barrier to tumor immune
infiltration, while also acting as a ligand for immune inhibitory receptors. Transforming …

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 …

Plenty of feature selection methods are available in literature due to the availability of data
with hundreds of variables leading to data with very high dimension. Feature selection …

We present a comprehensive experimental study of 12 individual as well as 6 ensemble
methods for feature selection for classification tasks on environmental data, more specifically …

The recent advancements in science, engineering, and technology have facilitated huge
generation of datasets. These huge datasets contain noisy, redundant, and irrelevant …

Breast cancer is one of the primary causes of death among the women worldwide, and the
accurate diagnosis is one of the most significant steps in breast cancer treatment. Data …

In many data analysis tasks, one is often confronted with very high dimensional data.
Feature selection techniques are designed to find the relevant feature subset of the original …

This is the first book to take a truly comprehensive look at clustering. It begins with an
introduction to cluster analysis and goes on to explore: proximity measures; hierarchical …