没有监督的特征选择与进化的稀疏性
Shixuan Zhou1, Yi Xiang2, Han Huang3
1School of Software Engineering, South China University of Technology, Guangzhou 510006, China.
概括
本研究介绍了无监督特征选择的进化稀疏性 (EVSP). EVSP有效地确定了最佳的功能数量,克服了现有方法的局限性,并提高了基准数据集的性能.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 计算机科学 计算机科学
背景情况:
- 对于无监督的特征选择,l2,0标准至关重要.
- 现有的算法在自动稀疏性确定方面扎,并且可以汇聚到局部最佳值,选择更少的信息特性.
研究的目的:
- 提出一种新的无监督特征选择方法,即进化稀疏性 (EVSP).
- 通过自动确定稀疏度并避免局部最佳值来解决现有方法的局限性.
主要方法:
- EVSP将特征选择与稀疏投影矩阵和人口搜索集成在一起.
- 一个具有二进制编码的多目标进化算法递归地确定了最佳的稀疏性.
- 一个突变修复操作员指导人口演变,以获得高质量的解决方案.
主要成果:
- EVSP有效地确定了最佳的稀疏度水平.
- 该方法显著优于几种最先进的无监督特征选择技术.
- 在11个具有高维度和样本大小的基准数据集上进行了实验.
结论:
- 通过自动优化稀疏性,EVSP提供了一种有效的无监督特征选择方法.
- 拟议的方法通过避免微不足道的特征选择和提高整体性能来增强特征选择.
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