An efficient binary Gradient-based optimizer for feature selection
Feature selection (FS) is a classic and challenging optimization task in the field of machine learning and data mining. Gradient-based optimizer (GBO) is a recently developed metaheuristic with population-based characteristics inspired by gradient-based Newton's method that uses two main operat...
Main Authors: | Yugui Jiang, Qifang Luo, Yuanfei Wei, Laith Abualigah, Yongquan Zhou |
---|---|
Format: | Article |
Language: | English |
Published: |
AIMS Press
2021-05-01
|
Series: | Mathematical Biosciences and Engineering |
Subjects: | |
Online Access: | http://www.aimspress.com/article/doi/10.3934/mbe.2021192?viewType=HTML |
Similar Items
-
Identification of Solar Photovoltaic Model Parameters Using an Improved Gradient-Based Optimization Algorithm With Chaotic Drifts
by: M. Premkumar, et al.
Published: (2021-01-01) -
An Efficient Binary Equilibrium Optimizer Algorithm for Feature Selection
by: Yuanyuan Gao, et al.
Published: (2020-01-01) -
Slip distribution inversion of seismic sub-fault dip iteration using gradient based optimizer algorithm
by: Leyang Wang, et al.
Published: (2024-03-01) -
Performance of Gradient-Based Optimizer on Charging Station Placement Problem
by: Essam H. Houssein, et al.
Published: (2021-11-01) -
Recent Methodology-Based Gradient-Based Optimizer for Economic Load Dispatch Problem
by: Sanchari Deb, et al.
Published: (2021-01-01)