Volume 11 - Volume 11
A Comparative Study of Classification of Occupational Stress in the Insurance Sector Using Machine Learning and Filter Feature Selection Techniques
Abstract
In recent years, occupational stress mining has become a widely exciting issue in the research field.
The primary purpose of this study is to analyze filter feature selection methods for the efficient
occupational stress classification model. We propose and examine seven different techniques of filter
feature selection such as Chi-Square, Information Gain, Information Gain Ratio, Correlation,
Principal Component Analysis, and Relief. The resultant selected features are then used with popular
classifiers like Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Artificial
Neural Network (ANN), and Gradient Boosted Trees (GBT) for detection of occupational stress in the
insurance sector. A survey-based psychological primary occupational stress data set is used to evaluate
the relative performance of these methods. This study effectively demonstrated the significance of filter feature selection methods and explained how accurately they could help classify stress levels. This study showed that the Correlation-based feature selection with the SVM classifier obtained the best performance compared to other filter feature selection methods and classification models.
Paper Details
PaperID: 2623
Author's Name: Arshad Hashmi, Waleed Ali and Shazia Tabassum
Volume: Volume 11
Issues: Volume 11
Keywords: Occupational Stress, Feature Selection, Ranking Method, Stress Classification, ANN, SV, RF.
Year: 2021
Month: October
Pages: 5781-5801