Performance analysis of intrusion detection systems using a feature selection method on the UNSW-NB15 dataset
Computer networks intrusion detection systems (IDSs) and intrusion prevention systems
(IPSs) are critical aspects that contribute to the success of an organization. Over the past …
(IPSs) are critical aspects that contribute to the success of an organization. Over the past …
[HTML][HTML] Benchmark for filter methods for feature selection in high-dimensional classification data
Feature selection is one of the most fundamental problems in machine learning and has
drawn increasing attention due to high-dimensional data sets emerging from different fields …
drawn increasing attention due to high-dimensional data sets emerging from different fields …
Battery health prediction using fusion-based feature selection and machine learning
State of health (SOH) is a key parameter to assess lithium-ion battery feasibility for
secondary usage applications. SOH estimation based on machine learning has attracted …
secondary usage applications. SOH estimation based on machine learning has attracted …
Feature selection using bare-bones particle swarm optimization with mutual information
X Song, Y Zhang, D Gong, X Sun - Pattern Recognition, 2021 - Elsevier
Feature selection (FS) is an important data processing method in pattern recognition and
data mining. Due to not considering characteristics of the FS problem itself, traditional …
data mining. Due to not considering characteristics of the FS problem itself, traditional …
Binary differential evolution with self-learning for multi-objective feature selection
Feature selection is an important data preprocessing method. This paper studies a new multi-
objective feature selection approach, called the Binary Differential Evolution with self …
objective feature selection approach, called the Binary Differential Evolution with self …
Whale optimization approaches for wrapper feature selection
Classification accuracy highly dependents on the nature of the features in a dataset which
may contain irrelevant or redundant data. The main aim of feature selection is to eliminate …
may contain irrelevant or redundant data. The main aim of feature selection is to eliminate …
Hybrid whale optimization algorithm with simulated annealing for feature selection
Hybrid metaheuristics are of the most interesting recent trends in optimization and memetic
algorithms. In this paper, two hybridization models are used to design different feature …
algorithms. In this paper, two hybridization models are used to design different feature …
Binary dragonfly optimization for feature selection using time-varying transfer functions
Abstract The Dragonfly Algorithm (DA) is a recently proposed heuristic search algorithm that
was shown to have excellent performance for numerous optimization problems. In this …
was shown to have excellent performance for numerous optimization problems. In this …
A study on metaheuristics approaches for gene selection in microarray data: algorithms, applications and open challenges
In the recent decades, researchers have introduced an abundance of feature selection
methods many of which are studied and analyzed over the high dimensional datasets …
methods many of which are studied and analyzed over the high dimensional datasets …
Particle swarm optimization for feature selection in classification: A multi-objective approach
Classification problems often have a large number of features in the data sets, but not all of
them are useful for classification. Irrelevant and redundant features may even reduce the …
them are useful for classification. Irrelevant and redundant features may even reduce the …