对特征选择的二进制算术优化算法
Min Xu1, Qixian Song1, Mingyang Xi1
1School of Physics and Electronic Engineering, Sichuan Normal University, Chengdu, 610101 Sichuan China.
Soft computing
|June 26, 2023
概括
本研究介绍了二进制算术优化算法 (BAOA),用于有效的特征选择. BAOA_S1LF在各种数据集中选择最佳特征方面表现出卓越的表现.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 优化优化 优化优化
背景情况:
- 特性选择对于数据预处理至关重要,但却提出了一个具有挑战性的组合优化问题.
- 现有的元启发式算法显示出希望,但算术优化算法仅限于连续问题.
研究的目的:
- 为有效的特征选择提出新的二进制算术优化算法 (BAOA).
- 为了提高搜索速度和提高逃离本地最佳功能选择的能力.
主要方法:
- 开发了6个BAOA,使用不同的传输函数将连续搜索空间转换为离散空间.
- 在六个额外的算法中与Lévy飞行集成的传输功能,以提高搜索效率和逃避局部最佳.
- 对20个加利福尼亚大学欧文 (UCI) 数据集的算法性能进行了评估.
主要成果:
- BAOA_S1LF 成为了在拟议的特征选择方法中最优秀的算法.
- 对26个UCI数据集的比较分析证实了BAOA_S1LF对其他元启发算法的优越性.
- BAOA_S1LF的源代码是公开可用的,用于可复制性和进一步研究.
结论:
- 拟议的BAOA,特别是BAOA_S1LF,提供了一种强大而有效的方法,用于组合优化特征选择.
- 与现有的元启发式算法相比,BAOA_S1LF在性能和效率方面具有显著的优势.
- 源代码的公开可用性促进了更广泛的采用和特征选择领域的进步.
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