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ANNPR
2008
Springer

Feature Ranking Ensembles for Facial Action Unit Classification

13 years 11 months ago
Feature Ranking Ensembles for Facial Action Unit Classification
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.
Terry Windeatt, Kaushala Dias
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2008
Where ANNPR
Authors Terry Windeatt, Kaushala Dias
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