Latent Preserving Generative Adversarial Network for Imbalance classification
| Autor: | Tanmoy Dam, Md Meftahul Ferdaus, Mahardhika Pratama, Sreenatha G. Anavatti, Senthilnath Jayavelu, Hussein Abbass |
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| Přispěvatelé: | The 29th IEEE International Conference on Image Processing Bordeaux, France 16-19 October 2022, Dam, Tanmoy, Ferdaus, MD Meftahul, Pratama, Mahardhika, Anavatti, Sreenatha, Jayavelu, Sentilnath, Abbas, Hussein |
| Rok vydání: | 2022 |
| Předmět: | |
| DOI: | 10.48550/arxiv.2209.01555 |
| Popis: | Many real-world classification problems have imbalanced frequency of class labels; a well-known issue known as the "class imbalance" problem. Classic classification algorithms tend to be biased towards the majority class, leaving the classifier vulnerable to misclassification of the minority class. While the literature is rich with methods to fix this problem, as the dimensionality of the problem increases, many of these methods do not scale-up and the cost of running them become prohibitive. In this paper, we present an end-to-end deep generative classifier. We propose a domain-constraint autoencoder to preserve the latent-space as prior for a generator, which is then used to play an adversarial game with two other deep networks, a discriminator and a classifier. Extensive experiments are carried out on three different multi-class imbalanced problems and a comparison with state-of-the-art methods. Experimental results confirmed the superiority of our method over popular algorithms in handling high-dimensional imbalanced classification problems. Our code is available on https://github.com/TanmDL/SLPPL-GAN. Comment: has been accepted for publication at The 29th IEEE International Conference on Image Processing (IEEE ICIP 2022) |
| Databáze: | OpenAIRE |
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