在分类中用于双目标特征选择的三目标方法
Ruwang Jiao1, Bing Xue2, Mengjie Zhang3
1School of Engineering and Computer Science, Victoria University of Wellington, Wellington, 6140, New Zealand ruwangjiao@gmail.com.
Evolutionary computation
|July 18, 2023
概括
这项研究引入了一种新的三目标方法,用于特征选择,平衡子集大小,分类准确性和特征多样性. 该方法增强了特征组合的探索,以提高分类性能.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 计算机科学 计算机科学
背景情况:
- 功能选择旨在将功能最小化,同时最大限度地提高分类性能,这是一个双重目标的挑战.
- 特征之间的相互作用需要探索超出目标空间性能之外的特征子集多样性.
研究的目的:
- 在分类中提出一种三目标方法,用于双目标特征选择.
- 将特征子集多样性纳入搜索空间作为第三个目标.
主要方法:
- 通过添加多样性目标,将双目标特征选择问题转换为三目标问题.
- 引入了新的初始化策略和后代繁殖操作员,以增强多样性和搜索能力.
主要成果:
- 提出的方法有效地平衡了最小化特征数量,最大化分类性能和探索多样化的特征子集.
- 在20个现实世界数据集上的实验结果表明,与现有方法相比,性能优越.
结论:
- 三目标方法通过考虑特征多样性来增强特征选择.
- 新的策略改善了对分类任务有前途的特征组合的探索.
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