Browsing KINDI Center for Computing Research by Subject "Ensemble learning"

Now showing items 1-4 of 4

    • A decomposition-based hybrid ensemble CNN framework for driver fatigue recognition 

      Li, Ruilin; Gao, Ruobin; Suganthan, Ponnuthurai Nagaratnam ( Elsevier Inc. , 2023 , Article)
      Electroencephalogram (EEG) has become increasingly popular in driver fatigue monitoring systems. Several decomposition methods have been attempted to analyze the EEG signals that are complex, nonlinear and non-stationary ...

    • Ensemble deep learning: A review 

      Ganaie, M. A.; Hu, Minghui; Malik, A. K.; Tanveer, M.; Suganthan, P. N. ( Elsevier Ltd , 2022 , Other)
      Ensemble learning combines several individual models to obtain better generalization performance. Currently, deep learning architectures are showing better performance compared to the shallow or traditional models. Deep ...

    • Graph ensemble deep random vector functional link network for traffic forecasting 

      Du, Liang; Gao, Ruobin; Suganthan, Ponnuthurai Nagaratnam; Wang, David Z.W. (2022 , Article)
      Traffic forecasting is crucial to achieving a smart city as it facilitates public transportation management, autonomous driving, and the resource relocation of the sharing economy. Traffic forecasting belongs to the ...

    • Oblique and rotation double random forest 

      Ganaie, M. A.; Tanveer, M.; Suganthan, P. N.; Snasel, V. ( Elsevier Ltd , 2022 , Article)
      Random Forest is an ensemble of decision trees based on the bagging and random subspace concepts. As suggested by Breiman, the strength of unstable learners and the diversity among them are the ensemble models’ core strength. ...