FREL: A stable feature selection algorithm

Yun Li, Jennie Si, Guojing Zhou, Shasha Huang, Songcan Chen

Research output: Contribution to journalArticlepeer-review

61 Scopus citations


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.

Original languageEnglish (US)
Article number6876214
Pages (from-to)1388-1402
Number of pages15
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number7
StatePublished - Jul 1 2015


  • Energy-based learning
  • ensemble
  • feature selection
  • feature weighting
  • uniform weighting stability

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


Dive into the research topics of 'FREL: A stable feature selection algorithm'. Together they form a unique fingerprint.

Cite this