A Generative Neighborhood-Based Deep Autoencoder with an Extended Loss Function for Robust Imbalanced Classification-Reference-Cited by-同舟云学术

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A Generative Neighborhood-Based Deep Autoencoder with an Extended Loss Function for Robust Imbalanced Classification

Published:2023-10-29 Issue: Volume:27 Page:1015-1019
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Container-title:2023 57th Asilomar Conference on Signals, Systems, and Computers
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Author:

Troullinou E.¹,Tsagkatakis G.¹,Losonczy A.²,Poirazi P.³,Tsakalides P.¹

Affiliation:

1. University of Crete,Department of Computer Science,Heraklion,Greece,70013

2. Columbia University Medical Center,Department of Neuroscience,New York,USA

3. Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology Hellas,Heraklion,Greece,70013

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10476702/10476779/10476931.pdf?arnumber=10476931

Reference16 articles.

1. Data imbalance: effects and solutions for classification of large and highly imbalanced data;Somasundaram,2016

2. Generative adversarial nets;Goodfellow;Advances in neural information processing systems,2014

3. Loss Functions of Generative Adversarial Networks (GANs): Opportunities and Challenges

4. Dice loss for data-imbalanced nlp tasks;Li;arXiv preprint,2019

5. Deep Generative Model for Robust Imbalance Classification

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