Dynamic Multi-Level Competition Learning-Based Dual-Task Optimization for High-Dimensional Feature Selection

Dynamic Multi-Level Competition Learning-Based Dual-Task Optimization for High-Dimensional Feature Selection

Feature selection (FS) is a critical task in data science and machine learning, presenting significant challenges in high-dimensional settings due to the complexity and noise inherent in large feature sets. To address these issues, this paper proposes a Dynamic Multi-Level Competition Learning-Based...

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Bibliographic Details
Main Authors: Weiwei Zhang, Yiwei Zhao, Chunbo Yuan, Xiaolong Chen, Wenzhao Liu, Yingjie Feng, Yongxin Feng, Jinchu Yang, Meng Li
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Feature selection (FS)
high-dimensional
fisher score
competitive swarm optimization (CSO)
Online Access:https://ieeexplore.ieee.org/document/10776776/
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https://ieeexplore.ieee.org/document/10776776/

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