Feature selection is a preprocessing technique that identifies the key features of a given
problem. It has traditionally been applied in a wide range of problems that include biological …

Ensemble learning is a prolific field in Machine Learning since it is based on the assumption
that combining the output of multiple models is better than using a single model, and it …

This paper offers a comprehensive approach to feature selection in the scope of
classification problems, explaining the foundations, real application problems and the …

In an era of growing data complexity and volume and the advent of big data, feature
selection has a key role to play in helping reduce high-dimensionality in machine learning …

Image analysis is a prolific field of research which has been broadly studied in the last
decades, successfully applied to a great number of disciplines. Since the apparition of Big …

Pattern analysis often requires a pre-processing stage for extracting or selecting features in
order to help the classification, prediction, or clustering stage discriminate or represent the …

Dry eye disease (DED) has a prevalence of between 5 and 50%, depending on the
diagnostic criteria used and population under study. However, it remains one of the most …

Feature selection is employed to reduce the feature dimensions and computational
complexity by eliminating irrelevant and redundant features. A vast amount of increasing …

Fast and effective oil spill detection systems are crucial to ensure a proper response to
environmental emergencies caused by hydrocarbon pollution on the ocean's surface …

This review presents and discusses the contribution of machine learning techniques for
diagnosis and disease monitoring in the context of clinical vision science. Many ocular …