Bagging Based Feature Selection for Dimensional Affect Recognition in the continuous Emotion Space

DOI

10.2991/icmt-13.2013.172How to use a DOI?

Keywords

Affect dimensions · Feature selection · Bagging · Correlation based feature selection

Abstract

This paper exploits the Bagging based feature selection method on the baseline audio features provided by AVEC2012 challenge competition. The selected features are input to SVR and RVM regression models, respectively, to estimate the affect dimensions arousal, valence, expectation, and power embedded in the audio speech. Experiments have been carried out on the word based and frame based baseline features, respectively, and the Pearson correlations between the estimated affect dimensions and their ground-truth labels are compared to those from the traditional correlation based feature selection (CFS) method with BestFirst or sequential floating forward selection (SFFS) algorithm. Experimental results show that both on word based and frame based baseline feature selection obtains the best accuracy in estimating the affect dimensions, while keeping the lowest number of features.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Chen Aihua
AU  - Yuan Shuai
AU  - Jiang Dongmei
PY  - 2013/11
DA  - 2013/11
TI  - Bagging Based Feature Selection for Dimensional Affect Recognition in the continuous Emotion Space
BT  - Proceedings of 3rd International Conference on Multimedia Technology(ICMT-13)
PB  - Atlantis Press
SP  - 1397
EP  - 1404
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmt-13.2013.172
DO  - 10.2991/icmt-13.2013.172
ID  - Aihua2013/11
ER  -