Feature Selection on Thermal-stress Dataset
This work addresses stress recognition systems for health monitoring, but it is incremental as it applies existing feature selection methods to a specific dataset.
The study tackled stress classification by selecting features from thermal-stress data using three techniques, finding that a genetic algorithm with ANNs improved prediction accuracy by 19.1% over the baseline.
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.