Maximizing the classification accuracy and minimizing the number of selected features are
two primary objectives in feature selection, which is inherently a multiobjective task …

Feature selection (FS) is a popular data pre-processing technique in machine learning to
extract the optimal features to maintain or increase the classification accuracy of the dataset …

Climate change is causing an increase in the frequency and severity of floods in various
regions globally, raising concerns about the efficacy of evacuation planning strategies that …

As a crucial data preprocessing method in data mining, feature selection (FS) can be
regarded as a bi-objective optimization problem that aims to maximize classification …

In recent years, feature selection research has quickly advanced to keep up with the age of
developing expert systems. This is because the applications of these systems sometimes …

Feature selection (FS) methods play essential roles in different machine learning
applications. Several FS methods have been developed; however, those FS methods that …

Feature selection (FS) is an indispensable activity in machine learning, whose purpose is to
identify relevant predictive values from a high-dimensional feature space to improve …

Abstract Land Use/Land Cover (LULC) classification using hyperspectral images in remote
sensing is a leading technology. However, LULC classification using hyperspectral images …

The multi-objective grasshopper optimization algorithm (MOGOA) is a relatively new
algorithm inspired by the collective behavior of grasshoppers, which aims to solve multi …

The challenge of high-dimensional feature selection (FS) lies in the search technique, which
needs to consider both minimizing the size of feature subset and maximizing the …