Random forest-based approach for physiological functional variable selection for driver’s stress level classification
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DOI: 10.1007/s10260-018-0423-5
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References listed on IDEAS
- Gregorutti, Baptiste & Michel, Bertrand & Saint-Pierre, Philippe, 2015. "Grouped variable importance with random forests and application to multiple functional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 15-35.
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Cited by:
- Antonis Kostopoulos & Thodoris Garefalakis & Eva Michelaraki & Christos Katrakazas & George Yannis, 2024. "Modeling and Sustainability Implications of Harsh Driving Events: A Predictive Machine Learning Approach," Sustainability, MDPI, vol. 16(14), pages 1-20, July.
- Huiqin Chen & Hao Liu & Hailong Chen & Jing Huang, 2023. "Towards Sustainable Safe Driving: A Multimodal Fusion Method for Risk Level Recognition in Distracted Driving Status," Sustainability, MDPI, vol. 15(12), pages 1-22, June.
- Fabrizio Maturo & Rosanna Verde, 2023. "Supervised classification of curves via a combined use of functional data analysis and tree-based methods," Computational Statistics, Springer, vol. 38(1), pages 419-459, March.
- Susrutha Babu Sukhavasi & Suparshya Babu Sukhavasi & Khaled Elleithy & Ahmed El-Sayed & Abdelrahman Elleithy, 2022. "Deep Neural Network Approach for Pose, Illumination, and Occlusion Invariant Driver Emotion Detection," IJERPH, MDPI, vol. 19(4), pages 1-23, February.
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Keywords
Physiological signals; Functional data; Random forests; Recursive feature elimination; Wavelets; Grouped variable importance;All these keywords.
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