Motivation: In life sciences, interpretability of machine learning models is as important as
their prediction accuracy. Linear models are probably the most frequently used methods for …

Feature Selection for mixed data is an active research area with many applications in
practical problems where numerical and non-numerical features describe the objects of …

The electroencephalogram (EEG) is an informative neuroimaging tool for studying attention-
deficit/hyperactivity disorder (ADHD); one main goal is to characterize the EEG of children …

Combining the mutual information criterion with a forward feature selection strategy offers a
good trade-off between optimality of the selected feature subset and computation time …

Innovation capability (IC) is a fundamental firms' strategic asset to sustain competitive
advantage. In this article, relying on patent data, patents forward citations are used as proxy …

Software organizations are increasingly incorporating machine learning (ML) into their
product offerings, driving a need for new data management tools. Many of these tools …

Research on keystroke-based authentication has traditionally assumed human impostors
who generate forgeries by physically typing on the keyboard. With bots now well understood …

Vulnerability to energy poverty occurs when a household has difficulties to meet adequate
energy service levels. Recent energy crises manifested the consequences of energy …

A hybrid filter/wrapper feature subset selection algorithm for regression is proposed. First,
features are filtered by means of a relevance and redundancy filter using mutual information …

The brain is a network that mediates information processing through a wide range of states.
The extent of state diversity is a reflection of the entropy of the network. Here we measured …