Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review
Affective computing, a subcategory of artificial intelligence, detects, processes, interprets,
and mimics human emotions. Thanks to the continued advancement of portable non …
and mimics human emotions. Thanks to the continued advancement of portable non …
Metaheuristic algorithms on feature selection: A survey of one decade of research (2009-2019)
Feature selection is a critical and prominent task in machine learning. To reduce the
dimension of the feature set while maintaining the accuracy of the performance is the main …
dimension of the feature set while maintaining the accuracy of the performance is the main …
An enhanced black widow optimization algorithm for feature selection
G Hu, B Du, X Wang, G Wei - Knowledge-Based Systems, 2022 - Elsevier
Feature selection is an important data processing method to reduce dimension of the raw
datasets while preserving the information as much as possible. In this paper, an enhanced …
datasets while preserving the information as much as possible. In this paper, an enhanced …
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 …
An efficient binary salp swarm algorithm with crossover scheme for feature selection problems
Searching for the (near) optimal subset of features is a challenging problem in the process of
feature selection (FS). In the literature, Swarm Intelligence (SI) algorithms show superior …
feature selection (FS). In the literature, Swarm Intelligence (SI) algorithms show superior …
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 …
Advances in nature-inspired metaheuristic optimization for feature selection problem: A comprehensive survey
The main objective of feature selection is to improve learning performance by selecting
concise and informative feature subsets, which presents a challenging task for machine …
concise and informative feature subsets, which presents a challenging task for machine …
Breast cancer prediction using a hybrid method based on butterfly optimization algorithm and ant lion optimizer
The design and development of a computer-based system for breast cancer detection are
largely reliant on feature selection techniques. These techniques are used to reduce the …
largely reliant on feature selection techniques. These techniques are used to reduce the …
The monarch butterfly optimization algorithm for solving feature selection problems
Feature selection (FS) is considered to be a hard optimization problem in data mining and
some artificial intelligence fields. It is a process where rather than studying all of the features …
some artificial intelligence fields. It is a process where rather than studying all of the features …
[HTML][HTML] Improved salp swarm algorithm for feature selection
Salp swarm algorithm (SSA) is a recently created bio-inspired optimization algorithm
presented in 2017 which is based on the swarming mechanism of salps. This paper tries to …
presented in 2017 which is based on the swarming mechanism of salps. This paper tries to …