IEEE Trans Neural Netw Learn Syst. 2015 Jul;26(7):1388-402. doi: 10.1109/TNNLS.2014.2341627. Epub 2014 Aug 12.
Two factors characterize a good feature selection algorithm: its accuracy and stability. This paper aims at introducing a new approach to stable feature selection algorithms. The innovation of this paper centers on a class of stable feature selection algorithms called feature weighting as regularized energy-based learning (FREL). Stability properties of FREL using L1 or L2 regularization are investigated. In addition, as a commonly adopted implementation strategy for enhanced stability, an ensemble FREL is proposed. A stability bound for the ensemble FREL is also presented. Our experiments using open source real microarray data, which are challenging high dimensionality small sample size problems demonstrate that our proposed ensemble FREL is not only stable but also achieves better or comparable accuracy than some other popular stable feature weighting methods.
它的准确性和稳定性。本文旨在介绍一种新的稳定特征选择算法。本文的创新点集中在一类称为基于特征加权的正则化能量学习（FREL）的稳定特征选择算法上。研究了使用 L1 或 L2 正则化的 FREL 的稳定性。此外，作为增强稳定性的常用实现策略，提出了集成 FREL。还提出了集成 FREL 的稳定性界。我们使用开源真实微阵列数据的实验，这些数据是具有挑战性的高维小样本量问题，表明我们提出的集成 FREL 不仅稳定，而且比其他一些流行的稳定特征加权方法具有更好或相当的准确性。
