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适应性模糊集群引导的简单,快速和高效的特征选择,用于高维和高度不平衡的二进制类生物信息学微阵列数据.

Yi Wei Tye1, XinYing Chew2, Umi Kalsom Yusof3

  • 1School of Computer Sciences, Universiti Sains Malaysia, Gelugor, Penang, 11800, Malaysia.

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概括
此摘要是机器生成的。

本研究介绍了自适应模糊集群引导的简单,快速和高效 (AFCG-SFE) 模型,用于在不平衡的微阵列数据中进行特征选择. AFCG-SFE有效地识别了歧视性特征,大大提高了分类性能并减少了数据的复杂性.

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二元不平衡分类是二元不平衡的分类.复杂性指标是指复杂性的指标.进化特征选择是进化特征的选择.模糊的聚类模糊的聚类.高维数据是高维数据.微阵列数据的数据

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科学领域:

  • 生物信息学是一种生物信息学.
  • 机器学习 机器学习
  • 数据挖掘 数据挖掘

背景情况:

  • 高维,不平衡的微阵列数据带来了诸如特征冗余和类重叠等挑战.
  • 这些问题使学习算法偏向于多数类,阻碍了准确的分类.

研究的目的:

  • 提出适应性模糊集群引导的简单,快速和高效 (AFCG-SFE) 功能选择模型.
  • 解决不平衡的微阵列数据中的特征冗余和类重叠问题,以改进分类.

主要方法:

  • AFCG-SFE使用双阶段模糊特征集群和相互信息来进行特征选择.
  • 它包含一个不平衡感应的惩罚-奖励健身功能,优化F-测量,G-平均值和AUC.
  • 通过特征分离性 (F1) 和类重叠 (N2) 强制执行由复杂性驱动的最小子集大小.

主要成果:

  • 在20个基准数据集中,AFCG-SFE实现了顶级分类性能.
  • 该模型显著减少了特征子集 (特征冗余减少>99%) 和类重叠 (N2).
  • 与基线相比,它显示了火车测试中最低的根平均平方误差 (RMSE).

结论:

  • AFCG-SFE模型提供了一个强大的解决方案,用于在高维,不平衡的微阵列数据中进行特征选择.
  • 它有效地平衡了特征歧视,减少冗余和少数群体的阶级敏感性.
  • 在分类准确性和特征子集缩小方面,AFCG-SFE的表现优于现有的方法.