Feature Ranking Ensembles for Facial Action Unit Classification

  • Terry Windeatt
  • Kaushala Dias
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5064)

Abstract

Recursive Feature Elimination RFE combined with feature-ranking is an effective technique for eliminating irrelevant features. In this paper, an ensemble of MLP base classifiers with feature-ranking based on the magnitude of MLP weights is proposed. This approach is compared experimentally with other popular feature-ranking methods, and with a Support Vector Classifier SVC. Experimental results on natural benchmark data and on a problem in facial action unit classification demonstrate that the MLP ensemble is relatively insensitive to the feature-ranking method, and simple ranking methods perform as well as more sophisticated schemes. The results are interpreted with the assistance of bias/variance of 0/1 loss function.

Keywords

Feature Selection Feature Subset Base Classifier Training Pattern Machine Learn Research 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Terry Windeatt
    • 1
  • Kaushala Dias
    • 1
  1. 1.Centre for Vision, Speech and Signal Proc (CVSSP)University of SurreyGuildfordUnited Kingdom

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