A review of the modification strategies of the nature inspired algorithms for feature selection problem
This survey is an effort to provide a research repository and a useful reference for
researchers to guide them when planning to develop new Nature-inspired Algorithms …
researchers to guide them when planning to develop new Nature-inspired Algorithms …
Intelligent EMG pattern recognition control method for upper-limb multifunctional prostheses: advances, current challenges, and future prospects
Upper-limb amputation imposes significant burden on amputees thereby restricting them
from fully exploring their environments during activities of daily living. The use of intelligent …
from fully exploring their environments during activities of daily living. The use of intelligent …
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 …
A survey on evolutionary computation approaches to feature selection
Feature selection is an important task in data mining and machine learning to reduce the
dimensionality of the data and increase the performance of an algorithm, such as a …
dimensionality of the data and increase the performance of an algorithm, such as a …
Multi-objective particle swarm optimization approach for cost-based feature selection in classification
Y Zhang, D Gong, J Cheng - IEEE/ACM transactions on …, 2015 - ieeexplore.ieee.org
Feature selection is an important data-preprocessing technique in classification problems
such as bioinformatics and signal processing. Generally, there are some situations where a …
such as bioinformatics and signal processing. Generally, there are some situations where a …
Feature selection using diversity-based multi-objective binary differential evolution
By identifying relevant features from the original data, feature selection methods can
maintain or improve the classification accuracy and reduce the dimensionality. Recently …
maintain or improve the classification accuracy and reduce the dimensionality. Recently …
A problem-specific non-dominated sorting genetic algorithm for supervised feature selection
Feature selection (FS), which plays an important role in classification tasks, has been
recently studied as a multi-objective optimization problem (MOP). In this paper, we consider …
recently studied as a multi-objective optimization problem (MOP). In this paper, we consider …
A hybrid algorithm using ant and bee colony optimization for feature selection and classification (AC-ABC Hybrid)
P Shunmugapriya, S Kanmani - Swarm and evolutionary computation, 2017 - Elsevier
Abstract Ant Colony Optimization (ACO) and Bee Colony Optimization (BCO) are famous
meta-heuristic search algorithms used in solving numerous combinatorial optimization …
meta-heuristic search algorithms used in solving numerous combinatorial optimization …
A novel wrapper-based feature subset selection method using modified binary differential evolution algorithm
O Tarkhaneh, TT Nguyen, S Mazaheri - Information Sciences, 2021 - Elsevier
In classification problems, normally there exists a large number of features, but not all of
them contributing to the improvement of classification performance. These redundant …
them contributing to the improvement of classification performance. These redundant …
Intrusion detection system for wireless mesh network using multiple support vector machine classifiers with genetic-algorithm-based feature selection
Security is a prime challenge in wireless mesh networks. The mesh nodes act as the
backbone of a network when confronting a wide variety of attacks. An intrusion detection …
backbone of a network when confronting a wide variety of attacks. An intrusion detection …