External vs. Internal SVM-RFE: The SVM-RFE Method Revisited and Applied to Emotion Recognition

Hela Daassi-Gnaba, Yacine Oussar


Support Vector Machines (SVM) are well known as a kernel based method mostly applied to classication. SVM-Recursive Feature Elimination (SVM- RFE) is a variable ranking and selection method dedicated to the design of SVM based classiers. In this paper, we propose to revisit the SVM-RFE method. We study two implementations of this feature selection method that we call External SVM-RFE and Internal SVM-RFE, respectively. The two implementations are ap- plied to rank and select acoustic features extracted from speech to design optimized linear SVM classiers that recognize speaker emotions. To show the efficiency of the External and Internal SVM-RFE methods, an extensive experimental study is presented. The SVM classiers were selected using a validation procedure that ensures strict speaker independence. The results are discussed and compared with those achieved when the features are ranked using the Gram-Schmidt procedure. Overall, the results achieve a recognition rate that exceeds 90%.


feature selection; classification; support vector machines (SVM); emotion recognition

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DOI: http://dx.doi.org/10.14311/NNW.2015.25.004


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