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一种基于高维基遗传数据的体的高效和交互式特征选择方法

Xiaoran Yan¹, Shilong Shang², Dongxi Li³

¹College of Artificial Intelligence, Taiyuan University of Technology, Taiyuan, Shanxi, China.

Scientific reports

|August 17, 2025

概括

我们介绍CEFS +,一种高效的特征选择方法,用于高维数据的copula. 这种方法显著提高了分类的准确性,超过了现有的方法,特别是对遗传数据集.

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:

高维数据给机器学习模型带来了挑战.
有效的特征选择对于提高模型性能和可解释性至关重要.
现有的特征选择方法可能会与复杂的,高阶特征相互作用作斗争.

研究的目的:

提出一个高效和互动的特征选择方法,使用的.
根据多变量相互信息的可分割性开发一种新的特征选择标准.
通过改进版本 (CEFS+) 提高拟议方法的稳定性和性能.

主要方法:

开发了CEFS (Copula Entropy Feature Selection),使用的来测量特征相关性并捕获全顺序相互作用.
结合特征特征和特征标签的相互信息与最大相关性最小冗余性战略.
拟议的CEFS+包含一个等级技术,以解决CEFS的不稳定性.
在五个不同的数据集上使用三个分类器评估CEFS和CEFS+.

主要成果:

在15个评估场景中,CEFS+在10个场景中实现了最高的分类准确性.
提出的方法显著优于其他六种常用的特征选择技术.
CEFS+在高维基遗传数据集上表现出特别高的有效性.

结论:

基于的特征选择为高维数据提供了有效的策略.
CEFS+提供了一种强大而准确的方法来提高分类性能.
这种方法对生物信息学等高维数据普遍存在的领域的应用具有前景.

关键词:

铜的变是因为的变.选择功能选择功能选择.高维数据是高维数据.机器学习是机器学习.互助信息互助信息互助信息互助信息

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