On Guo and Nixon's criterion for feature subset selection: Assumptions, implications, and alternative options

Kiran S. Balagani, Vir V. Phoha, S. S. Iyengar, N. Balakrishnan

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Guo and Nixon proposed a feature selection method based on maximizing I(x;Y), the multidimensional mutual information between feature vector x and class variable Y. Because computing I(x;Y) can be difficult in practice, Guo and Nixon proposed an approximation of I(x;Y) as the criterion for feature selection. We show that Guo and Nixon's criterion originates from approximating the joint probability distributions in I(x;Y) by second-order product distributions. We remark on the limitations of the approximation and discuss computationally attractive alternatives to compute I(x;Y).

Original languageEnglish (US)
Article number5395688
Pages (from-to)651-655
Number of pages5
JournalIEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans
Volume40
Issue number3
DOIs
StatePublished - May 2010
Externally publishedYes

Keywords

  • Entropic spanning graphs
  • Feature selection
  • Mutual information
  • Parzen window

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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