GAAE: a novel genetic algorithm based on autoencoder with ensemble classifiers for imbalanced healthcare data-Reference-Cited by-同舟云学术

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GAAE: a novel genetic algorithm based on autoencoder with ensemble classifiers for imbalanced healthcare data

Published:2022-07-13 Issue:1 Volume:79 Page:541-572
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Ram Pintu Kumar,Kuila Pratyay

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s11227-022-04679-x.pdf

Reference44 articles.

1. Ayar M, Isazadeh A, Gharehchopogh FS, Seyedi M (2022) Chaotic-based divide-and-conquer feature selection method and its application in cardiac arrhythmia classification. J Supercomput 78(4):5856–5882

2. Xiao Y, Wu J, Lin Z (2021) Cancer diagnosis using generative adversarial networks based on deep learning from imbalanced data. Comput Biol Med 135:104540

3. De Angeli K, Gao S, Danciu I, Durbin EB, Wu X-C, Stroup A, Doherty J, Schwartz S, Wiggins C, Damesyn M, Coyle L, Penberthy L, Tourassi GD, Yoon H-J (2022) Class imbalance in out-of-distribution datasets: Improving the robustness of the textcnn for the classification of rare cancer types. J Biomed Inform 125:103957

4. Bakhsh AA (2021) High-performance in classification of heart disease using advanced supercomputing technique with cluster-based enhanced deep genetic algorithm. J Supercomput 77(9):10540–10561

5. Ebenuwa SH, Sharif MS, Alazab M, Al-Nemrat A (2019) Variance ranking attributes selection techniques for binary classification problem in imbalance data. IEEE Access 7:24649–24666

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