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

Title:

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

Creator:

Daassi-Gnaba, Hela and Oussar, Yacine

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:0059fd7f-0d86-4e75-bdc3-b28e290e551e
uuid:0059fd7f-0d86-4e75-bdc3-b28e290e551e
doi:10.14311/NNW.2015.25.004

Subject:

Feature selection, classification, Support Vector Machines (SVM), and emotion recognition

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Support Vector Machines (SVM) are well known as a kernel based method mostly applied to classification. SVM-Recursive Feature Elimination (SVM- RFE) is a variable ranking and selection method dedicated to the design of SVM based classifiers. 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 applied to rank and select acoustic features extracted from speech to design optimized linear SVM classifiers that recognize speaker emotions. To show the efficiency of the External and Internal SVM-RFE methods, an extensive experimental study is presented. The SVM classifiers 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%.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2015 Volume:25 | Number:1

Harvested from:

CDK

Metadata only:

false