Feature Fusion for Improved Classification: Combining Dempster-Shafer Theory and Multiple CNN Architectures
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-70819-0_22
Reference22 articles.
1. Boulila, W., Ayadi, Z., Farah, I.R.: Sensitivity analysis approach to model epistemic and aleatory imperfection: application to land cover change prediction model. J. Comput. Sci. 23, 58–70 (2017)
2. Chen, X., et al.: Comprehensive evaluation of dam seepage safety combining deep learning with Dempster-Shafer evidence theory. Measurement 226, 114172 (2024). https://doi.org/10.1016/j.measurement.2024.114172
3. Dempster, A.P.: A generalization of Bayesian inference. J. Roy. Stat. Soc.: Ser. B (Methodol.) 30(2), 205–232 (1968)
4. Denœux, T.: Logistic regression, neural networks and Dempster-Shafer theory: a new perspective. Knowl.-Based Syst. 176, 54–67 (2019)
5. Ferchichi, A., Boulila, W., Farah, I.R.: Propagating aleatory and epistemic uncertainty in land cover change prediction process. Ecol. Inform. 37, 24–37 (2017)
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