This systematic review provides researchers interested in feature selection (FS) for
processing microarray data with comprehensive information about the main research …

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 …

Microarray data classification is a difficult challenge for machine learning researchers due to
its high number of features and the small sample sizes. Feature selection has been soon …

Abstract The era of Big Data has arrived along with large volume, complex and growing data
generated by many distinct sources. Nowadays, nearly every aspect of the modern society is …

In the last decade, ensemble learning has become a prolific discipline in pattern recognition,
based on the assumption that the combination of the output of several models obtains better …

Selecting a subset of relevant features is crucial to the analysis of high-dimensional datasets
coming from a number of application domains, such as biomedical data, document and …

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 …

Background and objective Medical data plays a decisive role in disease diagnosis. The
classification accuracy of high-dimensional datasets is often diminished by several …

Feature selection is often required as a preliminary step for many pattern recognition
problems. However, most of the existing algorithms only work in a centralized fashion, ie …