An ensemble-based regression model for perceived stress prediction using relevant personality traits / Chang Hon Fey - Chang , Hon Fey (2018)
This study compared various machine learning methods to develop an accurate predictive system to predict perceived stress in regression problem with relevant personality traits. The machine learning methods that were identified and being compared including the single regression models (Multiple Linear Regression, Support Vector Machine for regression, Elastic Net, Random Forest, Gaussian Process Regression, and Multilayer. Perceptron), homogeneous ensemble models (Bagging, Random Subspace, and Additive Regression), and heterogeneous ensemble models (Voting and Stacking). The dataset for the training and testing the predictive methods was taken from a study which the survey was distributed to the public in Melbourne, Australia and its surrounding districts. The selected predictors for perceived stress include gender and six personality traits, namely; mastery, positive affect, negative affect, life satisfaction, self-esteem, and perceived control of internal states. The predictive performances of all the predictive methods were compared, and the benchmark single model was identified. The ensemble instances with certain combinations of single models as base learners and with certain meta learners were proven to perform better than the benchmark single model. The implications and recommendations were discussed in this study.