IBDA: Improved Binary Dragonfly Algorithm With Evolutionary Population Dynamics and Adaptive Crossover for Feature Selection
Feature selection is an effective method to eliminate irrelevant, redundant and noisy features, which improves the performance of classification and reduces the computational burden in machine learning. In this paper, an improved binary dragonfly algorithm (IBDA) which extends from the conventional...
Main Authors: | Jiahui Li, Hui Kang, Geng Sun, Tie Feng, Wenqi Li, Wei Zhang, Bai Ji |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9113482/ |
Similar Items
-
Hybrid Binary Dragonfly Optimization Algorithm with Statistical Dependence for Feature Selection
by: Omar S. Qasim, et al.
Published: (2020-12-01) -
Bio-Inspired Feature Selection: An Improved Binary Particle Swarm Optimization Approach
by: Bai Ji, et al.
Published: (2020-01-01) -
A Hybrid Improved Dragonfly Algorithm for Feature Selection
by: Xueting Cui, et al.
Published: (2020-01-01) -
Computations of Flow past the Corrugated Airfoil of Drosophila Melanogaster at Ultra Low Reynolds Number
by: B. Rohit, et al.
Published: (2021-01-01) -
An Improved Real-Time Path Planning Method Based on Dragonfly Algorithm for Heterogeneous Multi-Robot System
by: Jianjun Ni, et al.
Published: (2020-01-01)