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 …

Disease diagnosis and prediction methods in biotechnology and medicine have significantly
advanced over time. Consequently, analyzing raw gene expression is crucial for identifying …

Enterprise credit risk prediction in the supply chain context is an important step for decision
making and early credit crisis warnings. Improving the prediction performance of this task is …

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

Because of the overgrowth of data, especially in text format, the value and importance of
multi‐label text classification have increased. Aside from this, preprocessing and particularly …

Feature selection ensemble methods are a recent approach aiming at adding diversity in
sets of selected features, improving performance and obtaining more robust and stable …

Mastery in forecasting the streamflow is of great importance in environmental and
sustainability research. Although many global-scale reanalysis products provide a new way …

Feature selection refers to the problem of finding the optimal subset of features by removing
irrelevant and redundant features to improve classification accuracy. The determination of …

The goal of Feature Selection-comprising filter, wrapper, and embedded approaches-is to
find the optimal feature subset for designated downstream tasks. Nevertheless, current …