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Identification of Optimal Emotion Classifier with Feature Selections Using Physiological Signals

Authors:
Byoung-Jun Park
Eun-Hye Jang
Sang-Hyeob Kim
Chul Huh
Jin-Hun Sohn

Keywords: emotion classification, physiologial signals, prototypes, feature selection, particle swarm optimization

Abstract:
The purpose of this study is to identify optimal algorithm for emotion classification which classify seven different emotional states (happiness, sadness, anger, fear, disgust, surprise, and stress) using physiological features. Skin temperature, photoplethysmography, electrodermal activity and electrocardiogram are recorded and analyzed as physiological signals. For classification problems of the seven emotions, the design involves two main phases. At the first phase, Particle Swarm Optimization selects P % of patterns to be treated as prototypes of seven emotional categories. At the second phase, the PSO is instrumental in the formation of a core set of features that constitute a collection of the most meaningful and highly discriminative elements of the original feature space. The study offers a complete algorithmic framework and demonstrates the effectiveness of the approach for a collection of selected data sets.

Pages: 224 to 229

Copyright: Copyright (c) IARIA, 2012

Publication date: January 30, 2012

Published in: conference

ISSN: 2308-4138

ISBN: 978-1-61208-177-9

Location: Valencia, Spain

Dates: from January 30, 2012 to February 4, 2012