RCE-IFE:递归的集群消除与集群内部特征消除
Cihan Kuzudisli1,2, Burcu Bakir-Gungor3, Bahjat Qaqish4
1Department of Computer Engineering, Faculty of Engineering, Hasan Kalyoncu University, Gaziantep, Turkey.
PeerJ. Computer science
|March 10, 2025
概括
递归集群消除与集群内部特征消除 (RCE-IFE) 方法有效地减少了高维生物数据. 它实现了强大的分类器性能,并通过更少的功能和更短的运行时间保持了功能相关性.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 机器学习在生物学中的应用
背景情况:
- 高维生物数据带来了计算和解释方面的挑战.
- 特性选择 (FS) 对于缩小维度至关重要.
- 特性分组是有效FS的基础技术.
研究的目的:
- 提出一种新的特征选择方法,即递归集群消除与集群内部特征消除 (RCE-IFE).
- 评估RCE-IFE在各种生物数据集上的缩小维度和歧视能力.
- 评估由RCE-IFE选择的特征的生物相关性和一致性.
主要方法:
- 开发了RCE-IFE,这是一种监督方法,它代特征分组和消除步骤.
- 评估了基因表达,miRNA表达,甲基化和元基因组学数据集的RCE-IFE.
- 将RCE-IFE与各种最先进的FS方法和特定领域的工具进行比较.
主要成果:
- 在具有最小特征和最短运行时间的表达式数据集上,RCE-IFE实现了0.85的平均曲线下面面积 (AUC).
- 在基因表达数据上平均AUC为0.76的表现优于几个已建立的FS方法 (MRMR,FCBF,IG,CMIM,SKB,XGBoost).
- 在使用更少的特征和在选择的特征中显示出高一致性时,在癌症数据集上表现出与多阶段可比的准确性.
结论:
- RCE-IFE提供了强大的分类器性能,并大大减少了功能大小.
- 该方法有效地保持了多个运行中的功能相关性和一致性.
- RCE-IFE为分析高维生物数据提供了强大的解决方案.
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