Building the classification model based on the genetic algorithm and the improved Bayesian method
Author:
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computational Theory and Mathematics,Computer Science Applications,Modeling and Simulation,Information Systems
Link
https://link.springer.com/content/pdf/10.1007/s41060-023-00436-2.pdf
Reference46 articles.
1. Bandyopadhyay, S., Maulik, U.: Non-parametric genetic clustering: comparison of validity indices. IEEE Trans. Syst. Man Cybern. Part C 31(1), 120–125 (2001)
2. Bandyopadhyay, S., Maulik, U.: Genetic clustering for automatic evolution of clusters and application to image classification. Pattern Recogn. 35(6), 1197–1208 (2002)
3. Behera, D.K., Das, M., Swetanisha, S.: Follower link prediction using the XGBoost classification model with multiple graph features. Wirel. Pers. Commun. 127, 695–714 (2021)
4. Behera, T.K., Khan, M.A., Bakshi, S.: Brain MR image classification using superpixel-based deep transfer learning. IEEE J. Biomed. Health Inform. (2022). https://doi.org/10.1109/JBHI.2022.3216270
5. Bidi,N., Elberrichi, Z.: Feature selection for text classification using genetic algorithms. In: 8th International Conference on Modelling, Identification and Control, Algiers, Algerial, pp. 806–810 (2016)
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