嵌套组合选择:一种有效的混合特征选择方法
Firuz Kamalov1, Hana Sulieman2, Sherif Moussa1
1Department of Electrical Engineering, Canadian University Dubai, Dubai, United Arab Emirates.
Heliyon
|October 9, 2023
概括
嵌套组合选择 (NES) 有效地识别了相关的特征,同时排除了无关,冗余和相关的特征. 这种新的特征选择方法超越了现有的算法,特别是对于多类数据集.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 生物信息学是一种生物信息学.
背景情况:
- 传统的特征选择方法难以区分相关特征,冗余特征和相关特征.
- 现有的混合过包装方法往往缺乏简单性,效率或精度.
研究的目的:
- 提出一种新的特征选择算法 - - 嵌套组合选择 (NES),它解决了现有方法的局限性.
- 通过区分相关特征与无关,冗余和相关特征来提高特征选择的精度和效率.
- 引入一种启发式方法来确定选定的特征的最佳数量.
主要方法:
- 嵌套组合选择 (NES),一种混合方法,结合了过器和包装方法.
- 开发一个强大的启发式,以实现最佳的特征子集识别.
- 与已建立的算法进行比较分析,如mRMR,Boruta和遗传算法.
主要成果:
- 在合成数据集上,NES实现了完美的精度,在现实数据上实现了近乎最佳的精度.
- 算法有效地将相关特征与无关,冗余和相关的特征分开.
- 与基准算法相比,NES表现出了显著的性能改进,特别是在多类数据集上.
结论:
- 嵌套组合选择 (NES) 为特征选择提供了一个简单,高效和精确的解决方案.
- 拟议的方法克服了特征选择的关键挑战,包括识别冗余和相关特征.
- NES代表了特征选择技术的重大进步,特别是对于复杂的多类数据.
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