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Dynamic Multi-Level Competition Learning-Based Dual-Task Optimization for High-Dimensional Feature Selection
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DOI：10.1109/ACCESS.2024.3510888.png)
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Abstract
En 中文
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 Dual-Task Optimization (DMLC-DTO) method. The approach introduces a dual-task generation strategy that uses the Fisher Score to generate sub-tasks, which aid the primary task in exploring the feature space more effectively and accelerating feature selection. The Dynamic Multi-Level Competition Learning-Based Optimization mechanism enhances population diversity by organizing particles into hierarchical levels, with lower-tier particles learning from those at higher tiers. This hierarchical structure is integrated with traditional Competitive Swarm Optimization (CSO) and with a dynamic factor regulating the balance between exploration and convergence. Furthermore, the Multi-Winner Based Knowledge Transfer method encourages inter-task learning by allowing particles at the same level across tasks to exchange knowledge and facilitate information transfer. Experiments on 13 high-dimensional, real-world datasets confirm the effectiveness and robustness of DMLC-DTO, showcasing its competitive performance in feature selection tasks.
Keywords:
Optimization
Classification algorithms
Heuristic algorithms
Convergence
Accuracy
Particle swarm optimization
Multitasking
Feature extraction
Computational efficiency
Knowledge transfer
Feature selection (FS)
high-dimensional
fisher score
competitive swarm optimization (CSO)
Journal
IF:
3.6
Papers:
9.7W
Citations:
29.4W