一个改进的二进制Walrus优化器与金色神经干扰和人口再生机制来解决特征选择问题
Yanyu Geng1,2, Ying Li1,2, Chunyan Deng1,2
1College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Biomimetics (Basel, Switzerland)
|August 28, 2024
概括
一个新的二进制金色神经精英基于对立的Walrus Optimizer (BGEPWO) 增强了功能选择. 这种元启发算法通过提高准确性和有效减少特征来改善数据挖掘.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 计算智能是一种计算智能.
背景情况:
- 特性选择 (FS) 对于高维数据的维度减少至关重要.
- 超启发式算法为有效的FS提供了强大的搜索功能.
- 现有方法在平衡勘探和开采方面面临挑战,并逃避当地最佳.
研究的目的:
- 提出一种新的改进的二进制海优化器 (WO) 算法,BGEPWO,用于增强特征选择.
- 为了提高人口多样性,算法稳定性和融合速度.
- 为了提高算法的逃离局部最佳的能力,并扩大搜索范围.
主要方法:
- 使用具有无限崩 (ICMIC) 的代混乱地图进行初始化,以实现多样性.
- 引入适应性运营商,以确保稳定性和勘探开发平衡.
- 实施人口再生机制,基于精英反对派的学习 (EOBL) 和优化黄金正弦策略.
主要成果:
- 与标准BWO和10个其他算法相比,BGEPWO在21个数据集中表现出卓越的性能.
- 在健身价值,选择特征数量和F1得分方面观察到显著的改善.
- 该算法实现了更高的准确性,更好的特征减少和更强的融合.
结论:
- BGEPWO有效地解决了高维数据中的特征选择挑战.
- 拟议的改进将导致增加人口多样性,平衡勘探开发,改善当地最佳避难.
- BGEPWO为机器学习和数据挖掘应用提供了强大而高效的解决方案.
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