基于S形灰狼优化器的FOX算法用于特征选择
Afi Kekeli Feda1, Moyosore Adegboye2, Oluwatayomi Rereloluwa Adegboye3
1Management Information System Department, European University of Lefke, Mersin, 10, Turkey.
Heliyon
|January 31, 2024
概括
增强的FOX-GWO算法通过集成灰狼优化器来改进特征选择,克服高维数据中的局部最佳值. 这提高了准确性,并有效地减少了维度.
科学领域:
- 计算智能是一种计算智能.
- 机器学习是机器学习.
- 数据科学是数据科学.
背景情况:
- 福克斯算法是一种元启发式算法,显示出有希望的结果,但在复杂问题中与局部最佳情况作斗争.
- 高维特征选择对于保留信息特征和丢弃不相关特征至关重要.
研究的目的:
- 为了增强FOX算法的利用能力,用于高维特征选择.
- 为了解决基本FOX算法的局限性,该算法被困在局部最佳状态中.
主要方法:
- 通过集成灰狼优化器 (GWO) 开发了一种改进的FOX算法,FOX-GWO.
- 引入了一个S形转移函数,以便在搜索过程中进行二进制探索.
- 在18个不同尺寸的数据集上进行了实验.
主要成果:
- 在18个数据集中,FOX-GWO实现了卓越的性能,平均精度提高了83.33%.
- 该算法在减少特征维度方面显示了61.11%的改进.
- 观察到平均72.22%的健身价值改善,表明高效的高维空间探索.
结论:
- 在功能选择方面,FOX-GWO有效地减轻了基本FOX算法的缺点.
- 改进的算法显示了促进复杂数据分析和提高模型预测准确性的巨大潜力.
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