Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pupil size variation as an indication of affective processing
International Journal of Human-Computer Studies - Application of affective computing in human—Computer interaction
Genetic Algorithm to Improve SVM Based Network Intrusion Detection System
AINA '05 Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 2
A Real-Time Human Stress Monitoring System Using Dynamic Bayesian Network
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
License to chill!: how to empower users to cope with stress
Proceedings of the 5th Nordic conference on Human-computer interaction: building bridges
What's Your Current Stress Level? Detection of Stress Patterns from GSR Sensor Data
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Detecting stress during real-world driving tasks using physiological sensors
IEEE Transactions on Intelligent Transportation Systems
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
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Stressis a major problem facing our world today and affects everyday lives providing motivation to develop an objective understanding of stress during typicalactivities. Physiological and physical response signals showing symptoms for stress can be used to provide hundreds of features. This encounters the problem of selecting appropriate features for stress recognition from a set of features that may include irrelevant, redundant or corrupted features. In addition, there is also a problem for selecting an appropriate computational classification model with optimal parameters to capture general stress patterns. The aim of this paper is to determine whether stress can be detected from individual-independent computational classification models with a genetic algorithm (GA) optimization scheme from sensor sourced stress response signals induced by reading text. The GA was used to select stress features, select a type of classifier and optimize the classifier's parameters for stress recognition. The classification models used were artificial neural networks (ANNs) and support vector machines (SVMs). Stress recognition rates obtained from an ANN and a SVM without a GA were 68% and 67% respectively. With a GA hybrid, the stress recognition rate improved to 89%. The improvement shows that a GA has the capacity to select salient stress features and define an optimal classification model with optimized parameter settings for stress recognition.