使用Sailfish优化器 (DESFO) 进行改进的差异演变,用于处理特征选择问题
Safaa M Azzam1, O E Emam1, Ahmed Sabry Abolaban2
1Department of Information Systems, Faculty of Computers and Artificial Intelligence, Helwan University, P.O. Box 11795, Helwan, Egypt.
Scientific reports
|June 12, 2024
概括
一个新的元启发算法,差异进化和帆鱼优化器 (DESFO),增强了机器学习的特征选择. 它有效地减少了数据的维度,并提高了分类准确性,优于其他现代算法.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据挖掘 数据挖掘
背景情况:
- 功能选择对于机器学习和数据挖掘至关重要,特别是在高维数据方面.
- 维度和局部最佳的诅咒给传统的特征选择算法带来了挑战.
- 超启发式技术为复杂的特征选择问题提供了一个有希望的解决方案.
研究的目的:
- 引入一种新的混合元启发算法,即差异进化和帆鱼优化器 (DESFO),用于有效的特征选择.
- 根据既有和现代的优化算法评估DESFO的性能.
- 证明DESFO在提高分类准确性和减少特征维度方面的能力.
主要方法:
- 提出的DESFO算法结合了差异进化和Sailfish优化器.
- 对其他九种现代算法进行了比较分析.
- 在14个多尺度基准上使用随机森林和关键最近邻居分类器来评估性能.
主要成果:
- 与所有其他测试的算法相比,DESFO实现了优越的性能.
- 该算法显著提高了分类准确性,随机森林达到85.7%,关键最近邻居达到100%.
- 适应性值显示出强的表现,随机森林的71%和关键最近邻居的85.7%.
结论:
- 在高维数据集中的特征选择中,DESFO算法非常有效.
- 在优化中,DESFO提供了一个强大的解决方案来克服局部最佳的局限性.
- 拟议的方法显示了提高机器学习模型性能的巨大潜力.
关键词:
分类 分类 分类 分类.不同进化的差异性进化.剥削 剥削 使用探索 探索 探索选择功能选择功能选择.地方搜索 地方搜索机器学习是机器学习.进行元启发式学习.优化优化 优化优化鱼是一种帆船鱼.斯旺姆情报部门的情报人员更多相关视频
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