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基因编程用于自动演变多个特征以进行分类.

Peng Wang1,2, Bing Xue3, Jing Liang4,1,5

  • 1School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China.

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概括
此摘要是机器生成的。

本研究介绍了用于同时选择和构建特征的遗传编程,以改进高维数据分类. 该方法通过创建互补特征来提高准确性和减少数据尺寸.

关键词:
分类 分类 分类 分类.缩小尺寸的缩小方式这是基因编程.

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科学领域:

  • 计算机科学 计算机科学
  • 机器学习 机器学习
  • 人工智能的人工智能

背景情况:

  • 高维数据分类面临来自大搜索空间和复杂特征交互的挑战.
  • 现有的方法,如特征选择或单独构建,可能会产生低于最佳的特征集.

研究的目的:

  • 调查使用遗传编程用于同时选择和构建特征.
  • 解决对高维数据集的分类任务.

主要方法:

  • 利用遗传编程进行综合特征选择和构建.
  • 在16个不同的数据集上测试了这种方法.
  • 与七种已建立的特征选择和施工技术进行性能比较.

主要成果:

  • 拟议的方法显著提高了数据集的分类准确性.
  • 实现了数据集维度的大幅减少.
  • 证明了同时选择和构建特征的有效性.

结论:

  • 遗传编程为同时选择和构建特征提供了一种强大的方法.
  • 综合方法克服了独立技术的局限性.
  • 所产生的互补特征导致了更好的分类性能和更少的维度.