FTDZOA:一个高效和稳健的FS方法与多策略援助
Fuqiang Chen1, Shitong Ye1, Lijuan Xu1
1Department of Artificial Intelligence, Guangzhou Huashang College, Guangzhou 511300, China.
Biomimetics (Basel, Switzerland)
|October 25, 2024
概括
这项研究增强了斑马优化算法 (ZOA) 在大数据中的特征选择 (FS). 改进的FTDZOA方法显著提高了分类准确性和大小减少任务的效率.
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 (FS) 对于大数据分析至关重要,减少冗余和优化资源.
- 传统的斑马优化算法 (ZOA) 面临着局部优化和FS中的缓慢融合等挑战.
研究的目的:
- 引入一个改进的斑马优化算法 (ZOA),命名为FTDZOA,用于优越的功能维度减少.
- 解决ZOA的局限性,包括局部最佳,有限的全球搜索和FS问题的缓慢融合.
主要方法:
- 整合一个分数订单搜索策略,以提高ZOA的开发能力.
- 纳入三重平均点指导策略,以提高ZOA的勘探能力.
- 引入差异化战略,通过整合信息差异来进一步提升ZOA的勘探能力.
主要成果:
- 在超过90%的测试数据集上,FTDZOA实现了更高的分类准确性.
- 与九种先进的FS方法相比,FTDZOA在执行时间方面表现出超过83%的获胜率.
- 基于FTDZOA的FS方法应用于23个FS问题,涉及低,中,高维度.
结论:
- FTDZOA是一种可靠,高性能,实用和强大的特征选择方法.
- 增强的算法有效地解决了ZOA在功能选择任务中的局限性.
- FTDZOA在精度和效率方面都提供了显著的改进,以减少维度.
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