This paper surveys main principles of feature selection and their recent applications in big
data bioinformatics. Instead of the commonly used categorization into filter, wrapper, and …

Feature selection techniques have been used as the workhorse in biomarker discovery
applications for a long time. Surprisingly, the stability of feature selection with respect to …

We propose a filtering feature selection framework that considers subsets of features as
paths in a graph, where a node is a feature and an edge indicates pairwise (customizable) …

We enhance the support vector machine recursive feature elimination (SVM-RFE) method
for gene selection by incorporating a minimum-redundancy maximum-relevancy (MRMR) …

Objective imaging-based biomarker discovery for psychiatric conditions is critical for
accurate diagnosis and treatment. Using a machine learning framework, this work …

Streaming feature selection (SFS), is the task of selecting the most informative features in
dealing with high-dimensional or incrementally growing problems. Several SFS algorithms …

In the last two decades, there have been massive advancements in high throughput
technologies, which resulted in the exponential growth of public repositories of gene …

There are growing concerns about the generalizability of machine learning classifiers in
neuroimaging. In order to evaluate this aspect across relatively large heterogeneous …

Using resting‐state functional magnetic resonance imaging, we test the hypothesis that
subjects with post‐traumatic stress disorder (PTSD) are characterized by reduced temporal …

Modelling river water level (WL) of a coastal catchment is much complex due to the tidal
influences on river WL. A hybrid machine learning model based on relevance vector …