Abstract
Stress is a major psychological disorder that conspicuously affects the psychological and physiological behavior of humans. Here, a dataset of MBA/MCA students is collected and analyzed to determine the overall rate of educational stress among these students. Seven different Swarm Intelligence (SI) based metaheuristic techniques, viz. Ant Lion Optimizer (ALO), Gray Wolf Optimization (GWO), Dragonfly Algorithm (DA), Satin Bowerbird Optimization (SBO), Harris Hawks Optimization (HHO), Butterfly Optimization Algorithm (BOA), Whale Optimization Algorithm (WOA) and one hybrid SI-based approach (WOA and Simulated Annealing (SA)) have been employed to find an optimal set of features for bi-objective stress diagnosis problem. As far as the stress classification rate is concerned, the hybrid swarm intelligence metaheuristic (WOA-SA) outperforms individual SI techniques as the use of simulated annealing in the amalgamation of WOA and SA improves the exploiting phase of the WOA. The results are also validated using the convergence rate and the Wilcoxon signed-rank test.
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Kaur, P., Gautam, R., Sharma, M. (2022). Feature Selection for Bi-objective Stress Classification Using Emerging Swarm Intelligence Metaheuristic Techniques. In: Gupta, D., Polkowski, Z., Khanna, A., Bhattacharyya, S., Castillo, O. (eds) Proceedings of Data Analytics and Management . Lecture Notes on Data Engineering and Communications Technologies, vol 91. Springer, Singapore. https://doi.org/10.1007/978-981-16-6285-0_29
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