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A novel pruning approach for bagging ensemble regression based on sparse representation

Khorashadi Zadeh, A. E ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1109/ICASSP40776.2020.9053835
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2020
  4. Abstract:
  5. This work aims to propose an approach for pruning a bagging ensemble regression (BER) model based on sparse representation, which we call sparse representation pruning (SRP). Firstly, a BER model with a specific number of subensembles should be trained. Then, the BER model is pruned by our sparse representation idea. For this type of regression problems, pruning means to remove the subensembles that do not have a significant effect on prediction of the output. The pruning problem is casted as a sparse representation problem, which will be solved by orthogonal matching pursuit (OMP) algorithm. Experiments show that the pruned BER with only 20% of the initial subensembles has a better generalization compared to a complete BER. © 2020 IEEE
  6. Keywords:
  7. Bagging Ensemble Regression ; Machine Learning ; Sparse Representation ; Speech communication ; Model-based OPC ; Orthogonal matching pursuit ; Regression problem ; Sparse representation ; Audio signal processing
  8. Source: 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020, 4 May 2020 through 8 May 2020 ; Volume 2020 , May , 2020 , Pages 4032-4036
  9. URL: https://www.researchgate.net/publication/341083183_A_Novel_Pruning_Approach_for_Bagging_Ensemble_Regression_Based_on_Sparse_Representation