TITLE: Feature Selection on Thermal-stress Dataset 

ABSTRACT: Physical symptoms caused by high stress commonly happen in our daily lives,
leading to the importance of stress recognition systems. This study aims to
improve stress classification by selecting appropriate features from
Thermal-stress data, ANUstressDB. We explored three different feature selection
techniques: correlation analysis, magnitude measure, and genetic algorithm.
Support Vector Machine (SVM) and Artificial Neural Network (ANN) models were
involved in measuring these three algorithms. Our result indicates that the
genetic algorithm combined with ANNs can improve the prediction accuracy by
19.1% compared to the baseline. Moreover, the magnitude measure performed best
among the three feature selection algorithms regarding the balance of
computation time and performance. These findings are likely to improve the
accuracy of current stress recognition systems.
Physical symptoms caused by high stress commonly happen in our daily lives,
leading to the importance of stress recognition systems. This study aims to
improve stress classification by selecting appropriate features from
Thermal-stress data, ANUstressDB. We explored three different feature selection
techniques: correlation analysis, magnitude measure, and genetic algorithm.
Support Vector Machine (SVM) and Artificial Neural Network (ANN) models were
involved in measuring these three algorithms. Our result indicates that the
genetic algorithm combined with ANNs can improve the prediction accuracy by
19.1% compared to the baseline. Moreover, the magnitude measure performed best
among the three feature selection algorithms regarding the balance of
computation time and performance. These findings are likely to improve the
accuracy of current stress recognition systems.
Feature Selection on Thermal-stress Dataset
http://arxiv.org/abs/2109.03755v1