Classification of High Dimensional Imbalanced Dataset via Game Theory-based Generative Adversarial Networks

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Mirabedini Sh.; KANGAVARI M.R.; MOHAMMADZADEH J.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF ELECTRONIC AND CYBER DEFENCE Year:2021 | Volume:9 | Issue:2 Page(s): 63-74

Download Full-Text

Persian Verion

View:

114

Download:

Cites:

Information Journal Paper

Title

Classification of High Dimensional Imbalanced Dataset via Game Theory-based Generative Adversarial Networks

Author(s)

Mirabedini Sh. | KANGAVARI M.R. | MOHAMMADZADEH J. | Issue Writer Certificate

Keywords

Generative Adversarial NetworksQ4

Deep Neural NetworksQ4

Image ClassificationQ4

Imbalanced DatasetQ4

Game TheoryQ4

Machine LearningQ4

Abstract

Game Theory uses mathematical models to analyze the methods of cooperation or competition of intelligent beings. Game Theory attempts to model the mathematical behavior of strategic interaction among rational decision-makers. The ultimate goal of this knowledge is to find the optimal strategy for the players. One of the newest ideas in the application of Game Theory in the field of artificial intelligence and Machine Learning is Generative Adversarial Networks. GANs consist of two parts, use Game Theory and compete with each other, making it possible for unsupervised or semi-supervised learning. In addition to generating data, these networks are also used to identify malicious software and software security, machine translation, and natural language processing, and to build a three-dimensional model of an image. However, GANs have a very long training time due to the high number of epochs and input parameters. In this paper, in order to solve the problem of long training time of these networks in the classification of imbalanced high-dimensional datasets, a solution is presented that first, GAN-based oversampling on minority classes. Then in order to improve the efficiency of the designed GAN, the mentioned network is parallelized and ensemble classification is done. The different scenarios performed on the classification of diabetic retinopathy dataset by the proposed method. The results showed the classification accuracy of 87%, the training time is reduced by 74%, which shows higher accuracy than the latest scientific advances.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops