Stable L2-regularized ensemble feature weighting

Yun Li, Shasha Huang, Songcan Chen, Jennie Si

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


When selecting features for knowledge discovery applications, stability is a highly desired property. By stability of feature selection, here it means that the feature selection outcomes vary only insignificantly if the respective data change slightly. Several stable feature selection methods have been proposed, but only with empirical evaluation of the stability. In this paper, we aim at providing a try to give an analysis for the stability of our ensemble feature weighting algorithm. As an example, a feature weighting method based on L2-regularized logistic loss and its ensembles using linear aggregation is introduced. Moreover, the detailed analysis for uniform stability and rotation invariance of the ensemble feature weighting method is presented. Additionally, some experiments were conducted using real-world microarray data sets. Results show that the proposed ensemble feature weighting methods preserved stability property while performing satisfactory classification. In most cases, at least one of them actually provided better or similar tradeoff between stability and classification when compared with other methods designed for boosting the stability.

Original languageEnglish (US)
Title of host publicationMultiple Classifier Systems - 11th International Workshop, MCS 2013, Proceedings
Number of pages12
StatePublished - 2013
Event11th International Workshop on Multiple Classifier Systems, MCS 2013 - Nanjing, China
Duration: May 15 2013May 17 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7872 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other11th International Workshop on Multiple Classifier Systems, MCS 2013

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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