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Robust dynamic classifier selection for remote sensing image classification

Meizhu Li (UGent) , Shaoguang Huang (UGent) and Aleksandra Pizurica (UGent)
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Abstract
Dynamic classifier selection (DCS) is a classification technique that, for each new sample to be classified, selects and uses the most competent classifier among a set of available ones. We here propose a novel DCS model (R-DCS) based on the robustness of its prediction: the extent to which the classifier can be altered without changing its prediction. In order to define and compute this robustness, we adopt methods from the theory of imprecise probabilities. Additionally, two selection strategies for R-DCS model are presented and are applied on remote sensing images. The experiment results demonstrate that our model successfully incorporates uncertainty with respect to the model parameters without losing the performance.

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MLA
Li, Meizhu, et al. “Robust Dynamic Classifier Selection for Remote Sensing Image Classification.” 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP), IEEE, 2019, pp. 101–05.
APA
Li, M., Huang, S., & Pizurica, A. (2019). Robust dynamic classifier selection for remote sensing image classification. In 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP) (pp. 101–105). Wuxi, China: IEEE.
Chicago author-date
Li, Meizhu, Shaoguang Huang, and Aleksandra Pizurica. 2019. “Robust Dynamic Classifier Selection for Remote Sensing Image Classification.” In 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP), 101–5. IEEE.
Chicago author-date (all authors)
Li, Meizhu, Shaoguang Huang, and Aleksandra Pizurica. 2019. “Robust Dynamic Classifier Selection for Remote Sensing Image Classification.” In 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP), 101–105. IEEE.
Vancouver
1.
Li M, Huang S, Pizurica A. Robust dynamic classifier selection for remote sensing image classification. In: 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP). IEEE; 2019. p. 101–5.
IEEE
[1]
M. Li, S. Huang, and A. Pizurica, “Robust dynamic classifier selection for remote sensing image classification,” in 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP), Wuxi, China, 2019, pp. 101–105.
@inproceedings{8621750,
  abstract     = {Dynamic classifier selection (DCS) is a classification technique that, for each new sample to be classified, selects and uses the most competent classifier among a set of available ones. We here propose a novel DCS model (R-DCS) based on the robustness of its prediction: the extent to which the classifier can be altered without changing its prediction. In order to define and compute this robustness, we adopt methods from the theory of imprecise probabilities. Additionally, two selection strategies for R-DCS model are presented and are applied on remote sensing images. The experiment results demonstrate that our model successfully incorporates uncertainty with respect to the model parameters without losing the performance.},
  author       = {Li, Meizhu and Huang, Shaoguang and Pizurica, Aleksandra},
  booktitle    = {2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP)},
  isbn         = {9781728136608},
  language     = {eng},
  location     = {Wuxi, China},
  pages        = {101--105},
  publisher    = {IEEE},
  title        = {Robust dynamic classifier selection for remote sensing image classification},
  url          = {http://dx.doi.org/10.1109/SIPROCESS.2019.8868599},
  year         = {2019},
}

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