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

Abstract Artificial Neural Networks (ANNs), sometimes also called models for deep learning,
are used extensively for the prediction of a range of environmental variables. While the …

Feature selection (FS) is an important preprocessing technique for dimensionality reduction
in classification problems. Particle swarm optimization (PSO) algorithms have been widely …

Feature selection is to reduce both the dimensionality of data and the classification error rate
(ie, increase the classification accuracy) of a learning algorithm. The two objectives are often …

Abstract The BATtle of the Attack Detection ALgorithms (BATADAL) is the most recent
competition on planning and management of water networks undertaken within the Water …

Feature selection is a pre-processing procedure of choosing the optimal feature subsets for
constructing model, yet it is difficult to satisfy the requirements of reducing number of …

This study presents an integrated framework of machine learning models (Artificial Neural
Network, Ensembled Learning Tree, Support Vector Machine, and Gaussian Process …

In this study, the authors propose a multi‐objective optimisation‐based feature selection
(FS) method for the detection of distributed denial of service (DDoS) attacks in an internet of …

Feature selection is a critical preprocess for constructing model in computer vision and
machine learning, yet it is difficult to simultaneously satisfy both reducing features' number …

In machine learning, an efficient classifier model design is mostly based on effective feature
extraction and appropriate feature selection. This work mainly focused on different optimized …