增强的通用正常分布优化器与高斯分布修复方法和cauchy反向学习的特征选择选择
Mohamed Ghetas1, Mohamed Abd Elaziz1, Mohamed Issa2,3,4
1Faculty of Computer Science and Engineering, Galala University, Suez, Egypt.
Scientific reports
|February 2, 2026
概括
本研究介绍了二进制自适应GNDO (BAGNDO),一种改进的特征选择方法,可以提高分类模型的性能. BAGNDO有效地解决了现有算法的局限性,在基准数据集上取得了卓越的结果.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 高维数据集往往含有噪音,冗余和无关的特征,降低了分类模型的性能.
- 特性选择对于识别最佳子集,提高模型效率和准确性至关重要.
- 现有的元启发算法,如通用正常分布优化 (GNDO),面临着诸如过早收和搜索失衡等挑战.
研究的目的:
- 为有效的特征选择提出一种新的二进制适应性 GNDO (BAGNDO) 框架.
- 提高元启发式算法的有效性,以解决噪音,高维数据的局限性.
- 提高分类准确度,减少机器学习模型中的特征子集大小.
主要方法:
- 开发了二进制自适应GNDO (BAGNDO) 框架,包括自适应考奇反向学习 (ACRL),精英池策略和基于高斯分布的最差解决方案修复 (GDWR).
- 评估了BAGNDO与九个最先进的元启发算法的性能.
- 在18个UCI基准数据集上测试了框架,使用基于封装的特征选择.
主要成果:
- 在18个基准数据集中,BAGNDO在14个基准数据集中实现了最高的分类准确性.
- 与其他算法相比,该框架始终产生了最紧的特征子集.
- 统计分析 (威尔科克森签名等级,弗里德曼测试) 证实了BAGNDO的显著优异表现.
结论:
- BAGNDO是一个强大而高效的解决方案,用于在高维数据集中基于封装的特征选择.
- 提议的改进有效地平衡了勘探和开采,克服了原来的GNDO算法的局限性.
- BAGNDO在优化对分类任务的特征选择方面取得了重大进展.
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