基于进化稀疏度调整的特征选择用于二元分类
Bach Hoai Nguyen1, Bing Xue2, Mengjie Zhang3
1School of Engineering and Computer Science, Faculty of Engineering, Victoria University of Wellington, Wellington, New Zealand Hoai.Bach.Nguyen@ecs.vuw.ac.nz.
Evolutionary computation
|August 22, 2024
概括
本研究引入了一种新的特征选择方法,该方法考虑了特征相互作用,并自动确定最佳数量的特征. 与现有方法相比,新方法提高了分类性能和效率.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 计算智能是一种计算智能.
背景情况:
- 通过减少维度来提高分类性能,特征选择至关重要.
- 嵌入式特征选择方法,如稀疏度规范化,在性能和计算时间之间提供了良好的平衡.
- 现有的方法经常输出特征排名,需要预定义的子集大小,并因忽略的相互作用而冒着冗余特征的选择风险.
研究的目的:
- 提出一个新的特征选择表示,以考虑特征交互.
- 开发一种算法,自动确定最佳的特征数量进行分类.
- 与现有的特征选择技术相比,提高分类性能和效率.
主要方法:
- 考虑特征交互的新型表示方式被开发出来.
- 使用微分进化 (DE) 算法来优化特征子集.
- 引入了一个独特的初始化机制,使DE能够探索各种特征子集大小.
主要成果:
- 拟议的算法成功地选择了合成数据集上的互补特征,避免了稀疏度规范化方法中常见的冗余问题.
- 在现实数据集上,该算法与已建立的包装,过器和嵌入式方法相比,显示出更高的分类性能.
- 该方法实现了与基于过器的特征选择技术相比较的计算效率.
结论:
- 拟议的特征选择方法有效地处理特征相互作用,并自动确定子集大小.
- 这种方法提供了更好的分类准确性和效率,优于现有的技术.
- 该算法在对分类任务的自动化和交互意识的特征选择方面取得了重大进展.
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