Feature selection (FS) is one of the basic data preprocessing steps in data mining and
machine learning. It is used to reduce feature size and increase model generalization. In …

In machine learning and data mining, feature selection (FS) is a traditional and complicated
optimization problem. Since the run time increases exponentially, FS is treated as an NP …

With recent advancements in data collection tools and the widespread use of intelligent
information systems, a huge amount of data streams with lots of redundant, irrelevant, and …

Feature selection is a crucial preprocessing step in the sphere of machine learning and data
mining, devoted to reducing the data dimensionality to improve the performance of learning …

Feature selection is an important pre-processing step for solving classification problems.
This problem is often solved by applying evolutionary algorithms in order to decrease the …

Grey Wolf Optimizer (GWO) simulates the grey wolves' nature in leadership and hunting
manners. GWO showed a good performance in the literature as a meta-heuristic algorithm …

Identification of the optimal subset of features for Feature Selection (FS) problems is a
demanding problem in machine learning and data mining. A trustworthy optimization …

Medical data classification is an important factor in improving diagnosis and treatment and
can assist physicians in making decisions about serious diseases by collecting symptoms …

Feature selection (FS) is a classic and challenging optimization task in the field of machine
learning and data mining. Gradient-based optimizer (GBO) is a recently developed …

Feature selection (FS) plays a crucial role as a pre-processing tool in data mining, especially
for real-world applications in medical fields; it has been utilized exponentially and becomes …