高性能极端学习机组分类的M估计激活函数
Fathi Alimi1, Adnan Khan2, Hameed Ali3
1Department of Chemistry, College of Science, University of Ha'il, P.O. Box 2440, Ha'il, 81441, Saudi Arabia.
Scientific reports
|September 1, 2025
概括
本研究引入了使用M估计理论的极端学习机器 (ELM) 的强大整体框架. 这种新方法提高了机器学习模型的准确性和应对网络安全应用中的噪音数据的弹性.
科学领域:
- 人工智能
- 机器学习
- 网络安全
背景情况:
- 机器学习,特别是软件定义网络中的人工智能,对于网络安全任务如流量监控和异常检测至关重要.
- 现有的组合方法经常与杂或受污染的数据作斗争,这限制了它们在现实世界的安全场景中的有效性.
研究的目的:
- 为极端学习机器 (ELM) 开发一个强大的整体框架,能够抵御数据不规则.
- 提高神经分类器的概括性,预测精度和稳定性.
主要方法:
- 提出了基于M估计理论的回降 ψ 激活函数的ELM新组合框架.
- 使用网格搜索来确定最佳的隐藏节点数量.
- 使用最小平方优化的组合输出,而不是用于精确参数估计的传统投票.
主要成果:
- 与现有的ELM数据集相比,拟议的方法在五个基准数据集中始终显示出更高的准确性和更小的差异.
- 通过严格的统计测试,包括Kruskal-Wallis和Dunn的后期分析,验证了性能增长.
- 该框架在概括性,预测精度和对数据不规则的适应性方面显著改善.
结论:
- 在受控组合中嵌入强大的基于M估计器的激活可以显著提高ELM性能.
- 开发的框架为机器学习应用设计高效和弹性的神经分类器提供了实质性的进步.
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