基于特征分组的特征选择方法的审查
Cihan Kuzudisli1,2, Burcu Bakir-Gungor3, Nurten Bulut3
1Department of Computer Engineering, Hasan Kalyoncu University, Gaziantep, Turkey.
PeerJ
|July 24, 2023
概括
本综述介绍了通过分组进行特征选择 (FS),这是一种新的方法,该方法评分的是特征组,而不是单个特征. 这种方法通过从类似组中选择代表性特征,有效地减少了高维数据.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 生物信息学是一种生物信息学.
背景情况:
- 高维数据在分析中存在挑战,原因是冗余和无关紧要.
- 传统的特征选择 (FS) 方法通常会单独评分特征,这些特征可能是次优的.
- 现有的审查涵盖了聚类和FS,但没有一个专门关注利用特征分组的FS技术.
结论:
- 通过分组进行特征选择为传统的个体特征评分方法提供了一个有希望的替代方案.
- 审查的方法在减少维度和改进数据分析方面表现出有效性.
- 这项工作为开发利用特征分组的新型特征选择策略提供了指导.
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